{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 实战练习之一 AI股票拟合算法"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输入必要的库"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas            as pd\n",
    "import tensorflow        as tf  \n",
    "import numpy             as np\n",
    "import matplotlib.pyplot as plt\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n",
    "\n",
    "from numpy                 import array\n",
    "from sklearn               import metrics\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models          import Sequential\n",
    "from tensorflow.keras.layers          import Dense,LSTM,Bidirectional\n",
    "\n",
    "from numpy.random   import seed\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ALLOW_THREADS',\n",
       " 'BUFSIZE',\n",
       " 'CLIP',\n",
       " 'DataSource',\n",
       " 'ERR_CALL',\n",
       " 'ERR_DEFAULT',\n",
       " 'ERR_IGNORE',\n",
       " 'ERR_LOG',\n",
       " 'ERR_PRINT',\n",
       " 'ERR_RAISE',\n",
       " 'ERR_WARN',\n",
       " 'FLOATING_POINT_SUPPORT',\n",
       " 'FPE_DIVIDEBYZERO',\n",
       " 'FPE_INVALID',\n",
       " 'FPE_OVERFLOW',\n",
       " 'FPE_UNDERFLOW',\n",
       " 'False_',\n",
       " 'Inf',\n",
       " 'Infinity',\n",
       " 'MAXDIMS',\n",
       " 'MAY_SHARE_BOUNDS',\n",
       " 'MAY_SHARE_EXACT',\n",
       " 'NAN',\n",
       " 'NINF',\n",
       " 'NZERO',\n",
       " 'NaN',\n",
       " 'PINF',\n",
       " 'PZERO',\n",
       " 'RAISE',\n",
       " 'RankWarning',\n",
       " 'SHIFT_DIVIDEBYZERO',\n",
       " 'SHIFT_INVALID',\n",
       " 'SHIFT_OVERFLOW',\n",
       " 'SHIFT_UNDERFLOW',\n",
       " 'ScalarType',\n",
       " 'True_',\n",
       " 'UFUNC_BUFSIZE_DEFAULT',\n",
       " 'UFUNC_PYVALS_NAME',\n",
       " 'WRAP',\n",
       " '_CopyMode',\n",
       " '_NoValue',\n",
       " '_UFUNC_API',\n",
       " '__NUMPY_SETUP__',\n",
       " '__all__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__config__',\n",
       " '__deprecated_attrs__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__expired_functions__',\n",
       " '__file__',\n",
       " '__former_attrs__',\n",
       " '__future_scalars__',\n",
       " '__getattr__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " '_add_newdoc_ufunc',\n",
       " '_builtins',\n",
       " '_core',\n",
       " '_distributor_init',\n",
       " '_financial_names',\n",
       " '_get_promotion_state',\n",
       " '_globals',\n",
       " '_int_extended_msg',\n",
       " '_mat',\n",
       " '_no_nep50_warning',\n",
       " '_pyinstaller_hooks_dir',\n",
       " '_pytesttester',\n",
       " '_set_promotion_state',\n",
       " '_specific_msg',\n",
       " '_typing',\n",
       " '_using_numpy2_behavior',\n",
       " '_utils',\n",
       " 'abs',\n",
       " 'absolute',\n",
       " 'add',\n",
       " 'add_docstring',\n",
       " 'add_newdoc',\n",
       " 'add_newdoc_ufunc',\n",
       " 'all',\n",
       " 'allclose',\n",
       " 'alltrue',\n",
       " 'amax',\n",
       " 'amin',\n",
       " 'angle',\n",
       " 'any',\n",
       " 'append',\n",
       " 'apply_along_axis',\n",
       " 'apply_over_axes',\n",
       " 'arange',\n",
       " 'arccos',\n",
       " 'arccosh',\n",
       " 'arcsin',\n",
       " 'arcsinh',\n",
       " 'arctan',\n",
       " 'arctan2',\n",
       " 'arctanh',\n",
       " 'argmax',\n",
       " 'argmin',\n",
       " 'argpartition',\n",
       " 'argsort',\n",
       " 'argwhere',\n",
       " 'around',\n",
       " 'array',\n",
       " 'array2string',\n",
       " 'array_equal',\n",
       " 'array_equiv',\n",
       " 'array_repr',\n",
       " 'array_split',\n",
       " 'array_str',\n",
       " 'asanyarray',\n",
       " 'asarray',\n",
       " 'asarray_chkfinite',\n",
       " 'ascontiguousarray',\n",
       " 'asfarray',\n",
       " 'asfortranarray',\n",
       " 'asmatrix',\n",
       " 'atleast_1d',\n",
       " 'atleast_2d',\n",
       " 'atleast_3d',\n",
       " 'average',\n",
       " 'bartlett',\n",
       " 'base_repr',\n",
       " 'binary_repr',\n",
       " 'bincount',\n",
       " 'bitwise_and',\n",
       " 'bitwise_not',\n",
       " 'bitwise_or',\n",
       " 'bitwise_xor',\n",
       " 'blackman',\n",
       " 'block',\n",
       " 'bmat',\n",
       " 'bool_',\n",
       " 'broadcast',\n",
       " 'broadcast_arrays',\n",
       " 'broadcast_shapes',\n",
       " 'broadcast_to',\n",
       " 'busday_count',\n",
       " 'busday_offset',\n",
       " 'busdaycalendar',\n",
       " 'byte',\n",
       " 'byte_bounds',\n",
       " 'bytes_',\n",
       " 'c_',\n",
       " 'can_cast',\n",
       " 'cast',\n",
       " 'cbrt',\n",
       " 'cdouble',\n",
       " 'ceil',\n",
       " 'cfloat',\n",
       " 'char',\n",
       " 'character',\n",
       " 'chararray',\n",
       " 'choose',\n",
       " 'clip',\n",
       " 'clongdouble',\n",
       " 'clongfloat',\n",
       " 'column_stack',\n",
       " 'common_type',\n",
       " 'compare_chararrays',\n",
       " 'compat',\n",
       " 'complex128',\n",
       " 'complex64',\n",
       " 'complex_',\n",
       " 'complexfloating',\n",
       " 'compress',\n",
       " 'concatenate',\n",
       " 'conj',\n",
       " 'conjugate',\n",
       " 'convolve',\n",
       " 'copy',\n",
       " 'copysign',\n",
       " 'copyto',\n",
       " 'corrcoef',\n",
       " 'correlate',\n",
       " 'cos',\n",
       " 'cosh',\n",
       " 'count_nonzero',\n",
       " 'cov',\n",
       " 'cross',\n",
       " 'csingle',\n",
       " 'ctypeslib',\n",
       " 'cumprod',\n",
       " 'cumproduct',\n",
       " 'cumsum',\n",
       " 'datetime64',\n",
       " 'datetime_as_string',\n",
       " 'datetime_data',\n",
       " 'deg2rad',\n",
       " 'degrees',\n",
       " 'delete',\n",
       " 'deprecate',\n",
       " 'deprecate_with_doc',\n",
       " 'diag',\n",
       " 'diag_indices',\n",
       " 'diag_indices_from',\n",
       " 'diagflat',\n",
       " 'diagonal',\n",
       " 'diff',\n",
       " 'digitize',\n",
       " 'disp',\n",
       " 'divide',\n",
       " 'divmod',\n",
       " 'dot',\n",
       " 'double',\n",
       " 'dsplit',\n",
       " 'dstack',\n",
       " 'dtype',\n",
       " 'dtypes',\n",
       " 'e',\n",
       " 'ediff1d',\n",
       " 'einsum',\n",
       " 'einsum_path',\n",
       " 'emath',\n",
       " 'empty',\n",
       " 'empty_like',\n",
       " 'equal',\n",
       " 'errstate',\n",
       " 'euler_gamma',\n",
       " 'exceptions',\n",
       " 'exp',\n",
       " 'exp2',\n",
       " 'expand_dims',\n",
       " 'expm1',\n",
       " 'expm1x',\n",
       " 'extract',\n",
       " 'eye',\n",
       " 'fabs',\n",
       " 'fastCopyAndTranspose',\n",
       " 'fft',\n",
       " 'fill_diagonal',\n",
       " 'find_common_type',\n",
       " 'finfo',\n",
       " 'fix',\n",
       " 'flatiter',\n",
       " 'flatnonzero',\n",
       " 'flexible',\n",
       " 'flip',\n",
       " 'fliplr',\n",
       " 'flipud',\n",
       " 'float16',\n",
       " 'float32',\n",
       " 'float64',\n",
       " 'float_',\n",
       " 'float_power',\n",
       " 'floating',\n",
       " 'floor',\n",
       " 'floor_divide',\n",
       " 'fmax',\n",
       " 'fmin',\n",
       " 'fmod',\n",
       " 'format_float_positional',\n",
       " 'format_float_scientific',\n",
       " 'format_parser',\n",
       " 'frexp',\n",
       " 'from_dlpack',\n",
       " 'frombuffer',\n",
       " 'fromfile',\n",
       " 'fromfunction',\n",
       " 'fromiter',\n",
       " 'frompyfunc',\n",
       " 'fromregex',\n",
       " 'fromstring',\n",
       " 'full',\n",
       " 'full_like',\n",
       " 'gcd',\n",
       " 'generic',\n",
       " 'genfromtxt',\n",
       " 'geomspace',\n",
       " 'get_array_wrap',\n",
       " 'get_include',\n",
       " 'get_printoptions',\n",
       " 'getbufsize',\n",
       " 'geterr',\n",
       " 'geterrcall',\n",
       " 'geterrobj',\n",
       " 'gradient',\n",
       " 'greater',\n",
       " 'greater_equal',\n",
       " 'half',\n",
       " 'hamming',\n",
       " 'hanning',\n",
       " 'heaviside',\n",
       " 'histogram',\n",
       " 'histogram2d',\n",
       " 'histogram_bin_edges',\n",
       " 'histogramdd',\n",
       " 'hsplit',\n",
       " 'hstack',\n",
       " 'hypot',\n",
       " 'i0',\n",
       " 'identity',\n",
       " 'iinfo',\n",
       " 'imag',\n",
       " 'in1d',\n",
       " 'index_exp',\n",
       " 'indices',\n",
       " 'inexact',\n",
       " 'inf',\n",
       " 'info',\n",
       " 'infty',\n",
       " 'inner',\n",
       " 'insert',\n",
       " 'int16',\n",
       " 'int32',\n",
       " 'int64',\n",
       " 'int8',\n",
       " 'int_',\n",
       " 'intc',\n",
       " 'integer',\n",
       " 'interp',\n",
       " 'intersect1d',\n",
       " 'intp',\n",
       " 'invert',\n",
       " 'is_busday',\n",
       " 'isclose',\n",
       " 'iscomplex',\n",
       " 'iscomplexobj',\n",
       " 'isfinite',\n",
       " 'isfortran',\n",
       " 'isin',\n",
       " 'isinf',\n",
       " 'isnan',\n",
       " 'isnat',\n",
       " 'isneginf',\n",
       " 'isposinf',\n",
       " 'isreal',\n",
       " 'isrealobj',\n",
       " 'isscalar',\n",
       " 'issctype',\n",
       " 'issubclass_',\n",
       " 'issubdtype',\n",
       " 'issubsctype',\n",
       " 'iterable',\n",
       " 'ix_',\n",
       " 'kaiser',\n",
       " 'kron',\n",
       " 'lcm',\n",
       " 'ldexp',\n",
       " 'left_shift',\n",
       " 'less',\n",
       " 'less_equal',\n",
       " 'lexsort',\n",
       " 'lib',\n",
       " 'linalg',\n",
       " 'linspace',\n",
       " 'little_endian',\n",
       " 'load',\n",
       " 'loadtxt',\n",
       " 'log',\n",
       " 'log10',\n",
       " 'log1p',\n",
       " 'log2',\n",
       " 'logaddexp',\n",
       " 'logaddexp2',\n",
       " 'logical_and',\n",
       " 'logical_not',\n",
       " 'logical_or',\n",
       " 'logical_xor',\n",
       " 'logspace',\n",
       " 'longcomplex',\n",
       " 'longdouble',\n",
       " 'longfloat',\n",
       " 'longlong',\n",
       " 'lookfor',\n",
       " 'ma',\n",
       " 'mask_indices',\n",
       " 'mat',\n",
       " 'matmul',\n",
       " 'matrix',\n",
       " 'max',\n",
       " 'maximum',\n",
       " 'maximum_sctype',\n",
       " 'may_share_memory',\n",
       " 'mean',\n",
       " 'median',\n",
       " 'memmap',\n",
       " 'meshgrid',\n",
       " 'mgrid',\n",
       " 'min',\n",
       " 'min_scalar_type',\n",
       " 'minimum',\n",
       " 'mintypecode',\n",
       " 'mod',\n",
       " 'modf',\n",
       " 'moveaxis',\n",
       " 'msort',\n",
       " 'multiply',\n",
       " 'nan',\n",
       " 'nan_to_num',\n",
       " 'nanargmax',\n",
       " 'nanargmin',\n",
       " 'nancumprod',\n",
       " 'nancumsum',\n",
       " 'nanmax',\n",
       " 'nanmean',\n",
       " 'nanmedian',\n",
       " 'nanmin',\n",
       " 'nanpercentile',\n",
       " 'nanprod',\n",
       " 'nanquantile',\n",
       " 'nanstd',\n",
       " 'nansum',\n",
       " 'nanvar',\n",
       " 'nbytes',\n",
       " 'ndarray',\n",
       " 'ndenumerate',\n",
       " 'ndim',\n",
       " 'ndindex',\n",
       " 'nditer',\n",
       " 'negative',\n",
       " 'nested_iters',\n",
       " 'newaxis',\n",
       " 'nextafter',\n",
       " 'nonzero',\n",
       " 'not_equal',\n",
       " 'numarray',\n",
       " 'number',\n",
       " 'obj2sctype',\n",
       " 'object_',\n",
       " 'ogrid',\n",
       " 'oldnumeric',\n",
       " 'ones',\n",
       " 'ones_like',\n",
       " 'outer',\n",
       " 'packbits',\n",
       " 'pad',\n",
       " 'partition',\n",
       " 'percentile',\n",
       " 'pi',\n",
       " 'piecewise',\n",
       " 'place',\n",
       " 'poly',\n",
       " 'poly1d',\n",
       " 'polyadd',\n",
       " 'polyder',\n",
       " 'polydiv',\n",
       " 'polyfit',\n",
       " 'polyint',\n",
       " 'polymul',\n",
       " 'polynomial',\n",
       " 'polysub',\n",
       " 'polyval',\n",
       " 'positive',\n",
       " 'power',\n",
       " 'printoptions',\n",
       " 'prod',\n",
       " 'product',\n",
       " 'promote_types',\n",
       " 'ptp',\n",
       " 'put',\n",
       " 'put_along_axis',\n",
       " 'putmask',\n",
       " 'quantile',\n",
       " 'r_',\n",
       " 'rad2deg',\n",
       " 'radians',\n",
       " 'random',\n",
       " 'ravel',\n",
       " 'ravel_multi_index',\n",
       " 'real',\n",
       " 'real_if_close',\n",
       " 'rec',\n",
       " 'recarray',\n",
       " 'recfromcsv',\n",
       " 'recfromtxt',\n",
       " 'reciprocal',\n",
       " 'record',\n",
       " 'remainder',\n",
       " 'repeat',\n",
       " 'require',\n",
       " 'reshape',\n",
       " 'resize',\n",
       " 'result_type',\n",
       " 'right_shift',\n",
       " 'rint',\n",
       " 'roll',\n",
       " 'rollaxis',\n",
       " 'roots',\n",
       " 'rot90',\n",
       " 'round',\n",
       " 'round_',\n",
       " 'row_stack',\n",
       " 's_',\n",
       " 'safe_eval',\n",
       " 'save',\n",
       " 'savetxt',\n",
       " 'savez',\n",
       " 'savez_compressed',\n",
       " 'sctype2char',\n",
       " 'sctypeDict',\n",
       " 'sctypes',\n",
       " 'searchsorted',\n",
       " 'select',\n",
       " 'set_numeric_ops',\n",
       " 'set_printoptions',\n",
       " 'set_string_function',\n",
       " 'setbufsize',\n",
       " 'setdiff1d',\n",
       " 'seterr',\n",
       " 'seterrcall',\n",
       " 'seterrobj',\n",
       " 'setxor1d',\n",
       " 'shape',\n",
       " 'shares_memory',\n",
       " 'short',\n",
       " 'show_config',\n",
       " 'show_runtime',\n",
       " 'sign',\n",
       " 'signbit',\n",
       " 'signedinteger',\n",
       " 'sin',\n",
       " 'sinc',\n",
       " 'single',\n",
       " 'singlecomplex',\n",
       " 'sinh',\n",
       " 'size',\n",
       " 'sometrue',\n",
       " 'sort',\n",
       " 'sort_complex',\n",
       " 'source',\n",
       " 'spacing',\n",
       " 'split',\n",
       " 'sqrt',\n",
       " 'square',\n",
       " 'squeeze',\n",
       " 'stack',\n",
       " 'std',\n",
       " 'str_',\n",
       " 'string_',\n",
       " 'subtract',\n",
       " 'sum',\n",
       " 'swapaxes',\n",
       " 'take',\n",
       " 'take_along_axis',\n",
       " 'tan',\n",
       " 'tanh',\n",
       " 'tensordot',\n",
       " 'test',\n",
       " 'testing',\n",
       " 'tile',\n",
       " 'timedelta64',\n",
       " 'trace',\n",
       " 'tracemalloc_domain',\n",
       " 'transpose',\n",
       " 'trapz',\n",
       " 'tri',\n",
       " 'tril',\n",
       " 'tril_indices',\n",
       " 'tril_indices_from',\n",
       " 'trim_zeros',\n",
       " 'triu',\n",
       " 'triu_indices',\n",
       " 'triu_indices_from',\n",
       " 'true_divide',\n",
       " 'trunc',\n",
       " 'typecodes',\n",
       " 'typename',\n",
       " 'typing',\n",
       " 'ubyte',\n",
       " 'ufunc',\n",
       " 'uint',\n",
       " 'uint16',\n",
       " 'uint32',\n",
       " 'uint64',\n",
       " 'uint8',\n",
       " 'uintc',\n",
       " 'uintp',\n",
       " 'ulonglong',\n",
       " 'unicode_',\n",
       " 'union1d',\n",
       " 'unique',\n",
       " 'unpackbits',\n",
       " 'unravel_index',\n",
       " 'unsignedinteger',\n",
       " 'unwrap',\n",
       " 'ushort',\n",
       " 'vander',\n",
       " 'var',\n",
       " 'vdot',\n",
       " 'vectorize',\n",
       " 'version',\n",
       " 'void',\n",
       " 'vsplit',\n",
       " 'vstack',\n",
       " 'where',\n",
       " 'who',\n",
       " 'zeros',\n",
       " 'zeros_like']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__all__\n",
    "np.__dict__\n",
    "np.__doc__\n",
    "np.__file__\n",
    "#np.__package__\n",
    "dir(np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 导入股票模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入tushare\n",
    "import tushare as ts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['MailMerge',\n",
       " 'TraderAPI',\n",
       " '__author__',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " '__version__',\n",
       " 'bar',\n",
       " 'bdi',\n",
       " 'broker_tops',\n",
       " 'cap_tops',\n",
       " 'close_apis',\n",
       " 'codecs',\n",
       " 'coins',\n",
       " 'coins_bar',\n",
       " 'coins_snapshot',\n",
       " 'coins_tick',\n",
       " 'coins_trade',\n",
       " 'day_boxoffice',\n",
       " 'day_cinema',\n",
       " 'forecast_data',\n",
       " 'fund',\n",
       " 'fund_holdings',\n",
       " 'futures',\n",
       " 'get_apis',\n",
       " 'get_area_classified',\n",
       " 'get_balance_sheet',\n",
       " 'get_cash_flow',\n",
       " 'get_cashflow_data',\n",
       " 'get_cffex_daily',\n",
       " 'get_concept_classified',\n",
       " 'get_cpi',\n",
       " 'get_czce_daily',\n",
       " 'get_day_all',\n",
       " 'get_dce_daily',\n",
       " 'get_debtpaying_data',\n",
       " 'get_deposit_rate',\n",
       " 'get_fund_info',\n",
       " 'get_future_daily',\n",
       " 'get_gdp_contrib',\n",
       " 'get_gdp_for',\n",
       " 'get_gdp_pull',\n",
       " 'get_gdp_quarter',\n",
       " 'get_gdp_year',\n",
       " 'get_gem_classified',\n",
       " 'get_gold_and_foreign_reserves',\n",
       " 'get_growth_data',\n",
       " 'get_h_data',\n",
       " 'get_hist_data',\n",
       " 'get_hists',\n",
       " 'get_hs300s',\n",
       " 'get_index',\n",
       " 'get_industry_classified',\n",
       " 'get_instrument',\n",
       " 'get_intlfuture',\n",
       " 'get_k_data',\n",
       " 'get_latest_news',\n",
       " 'get_loan_rate',\n",
       " 'get_markets',\n",
       " 'get_money_supply',\n",
       " 'get_money_supply_bal',\n",
       " 'get_nav_close',\n",
       " 'get_nav_grading',\n",
       " 'get_nav_history',\n",
       " 'get_nav_open',\n",
       " 'get_notices',\n",
       " 'get_operation_data',\n",
       " 'get_ppi',\n",
       " 'get_profit_data',\n",
       " 'get_profit_statement',\n",
       " 'get_realtime_quotes',\n",
       " 'get_report_data',\n",
       " 'get_rrr',\n",
       " 'get_shfe_daily',\n",
       " 'get_shfe_vwap',\n",
       " 'get_sina_dd',\n",
       " 'get_sme_classified',\n",
       " 'get_st_classified',\n",
       " 'get_stock_basics',\n",
       " 'get_suspended',\n",
       " 'get_sz50s',\n",
       " 'get_terminated',\n",
       " 'get_tick_data',\n",
       " 'get_today_all',\n",
       " 'get_today_ticks',\n",
       " 'get_token',\n",
       " 'get_zz500s',\n",
       " 'global_realtime',\n",
       " 'guba_sina',\n",
       " 'ht_subs',\n",
       " 'inst_detail',\n",
       " 'inst_tops',\n",
       " 'internet',\n",
       " 'is_holiday',\n",
       " 'latest_content',\n",
       " 'lpr_data',\n",
       " 'lpr_ma_data',\n",
       " 'margin_detail',\n",
       " 'margin_offset',\n",
       " 'margin_target',\n",
       " 'margin_zsl',\n",
       " 'moneyflow_hsgt',\n",
       " 'month_boxoffice',\n",
       " 'new_cbonds',\n",
       " 'new_stocks',\n",
       " 'notice_content',\n",
       " 'os',\n",
       " 'pledged_detail',\n",
       " 'pro',\n",
       " 'pro_api',\n",
       " 'pro_bar',\n",
       " 'profit_data',\n",
       " 'profit_divis',\n",
       " 'quotes',\n",
       " 'realtime_boxoffice',\n",
       " 'realtime_list',\n",
       " 'realtime_quote',\n",
       " 'realtime_tick',\n",
       " 'reset_instrument',\n",
       " 'set_token',\n",
       " 'sh_margin_details',\n",
       " 'sh_margins',\n",
       " 'shibor_data',\n",
       " 'shibor_ma_data',\n",
       " 'shibor_quote_data',\n",
       " 'stock',\n",
       " 'stock_issuance',\n",
       " 'stock_pledged',\n",
       " 'subs',\n",
       " 'sz_margin_details',\n",
       " 'sz_margins',\n",
       " 'tick',\n",
       " 'top10_holders',\n",
       " 'top_list',\n",
       " 'trade_cal',\n",
       " 'trader',\n",
       " 'util',\n",
       " 'xsg_data']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ts)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tushare 初始化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "\n",
    "# 设置 token 并初始化\n",
    "ts.set_token('7344ede0cb2ff5058820d61132e5fa84a7b19b1cb57149b5c9f9004b')\n",
    "pro = ts.pro_api()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function pro_api in module tushare.pro.data_pro:\n",
      "\n",
      "pro_api(token='', timeout=30)\n",
      "    初始化pro API,第一次可以通过ts.set_token('your token')来记录自己的token凭证，临时token可以通过本参数传入\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#pro=ts.get_hist_data('603722')\n",
    "help(ts.pro_api)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A股日线行情\n",
    "\n",
    "接口：daily，可以通过数据工具调试和查看数据   \n",
    "数据说明：交易日每天15点～16点之间入库。本接口是未复权行情，停牌期间不提供数据   \n",
    "调取说明：120积分每分钟内最多调取500次，每次6000条数据，相当于单次提取23年历史   \n",
    "描述：获取股票行情数据，或通过通用行情接口获取数据，包含了前后复权数据   \n",
    "\n",
    "<p><strong>输入参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>必选</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>股票代码（支持多个股票同时提取，逗号分隔）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>交易日期（YYYYMMDD）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>start_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>开始日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>end_date</td>\n",
    "<td>str</td>\n",
    "<td>N</td>\n",
    "<td>结束日期(YYYYMMDD)</td>\n",
    "</tr>\n",
    "</tbody>\n",
    "</table>\n",
    "\n",
    "\n",
    "<p><strong>注：日期都填YYYYMMDD格式，比如20181010</strong></p>\n",
    "<p><strong>输出参数</strong></p>\n",
    "<table>\n",
    "<thead>\n",
    "<tr>\n",
    "<th>名称</th>\n",
    "<th>类型</th>\n",
    "<th>描述</th>\n",
    "</tr>\n",
    "</thead>\n",
    "<tbody><tr>\n",
    "<td>ts_code</td>\n",
    "<td>str</td>\n",
    "<td>股票代码</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>trade_date</td>\n",
    "<td>str</td>\n",
    "<td>交易日期</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>open</td>\n",
    "<td>float</td>\n",
    "<td>开盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>high</td>\n",
    "<td>float</td>\n",
    "<td>最高价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>low</td>\n",
    "<td>float</td>\n",
    "<td>最低价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>close</td>\n",
    "<td>float</td>\n",
    "<td>收盘价</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pre_close</td>\n",
    "<td>float</td>\n",
    "<td>昨收价(前复权)</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>change</td>\n",
    "<td>float</td>\n",
    "<td>涨跌额</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>pct_chg</td>\n",
    "<td>float</td>\n",
    "<td>涨跌幅 （未复权，如果是复权请用 <a href=\"https://tushare.pro/document/2?doc_id=109\">通用行情接口</a> ）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>vol</td>\n",
    "<td>float</td>\n",
    "<td>成交量 （手）</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>amount</td>\n",
    "<td>float</td>\n",
    "<td>成交额 （千元）</td>\n",
    "</tr>\n",
    "</tbody></table>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### 初始化pro接口\n",
    "### 在这个网址https://tushare.pro/注册，并在此处获得token，https://tushare.pro/user/token\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000021.SZ   20241225  20.26  20.42  19.66  19.76      20.28   -0.52   \n",
      "1     000021.SZ   20241224  20.31  20.50  19.66  20.28      20.27    0.01   \n",
      "2     000021.SZ   20241223  20.88  21.68  20.20  20.27      20.23    0.04   \n",
      "3     000021.SZ   20241220  19.90  20.55  19.70  20.23      20.00    0.23   \n",
      "4     000021.SZ   20241219  19.16  20.07  19.08  20.00      19.44    0.56   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3603  000021.SZ   20100108  13.50  13.62  13.10  13.46      13.48   -0.02   \n",
      "3604  000021.SZ   20100107  13.34  13.75  13.21  13.48      13.31    0.17   \n",
      "3605  000021.SZ   20100106  13.40  13.86  13.31  13.31      13.40   -0.09   \n",
      "3606  000021.SZ   20100105  13.06  13.70  12.95  13.40      13.10    0.30   \n",
      "3607  000021.SZ   20100104  13.03  13.17  12.85  13.10      12.89    0.21   \n",
      "\n",
      "      pct_chg         vol        amount  \n",
      "0     -2.5641   496775.66  9.929593e+05  \n",
      "1      0.0493   735335.52  1.473577e+06  \n",
      "2      0.1977  1199854.36  2.509186e+06  \n",
      "3      1.1500   779340.05  1.575558e+06  \n",
      "4      2.8807   605595.48  1.195438e+06  \n",
      "...       ...         ...           ...  \n",
      "3603  -0.1500   135241.12  1.814568e+05  \n",
      "3604   1.2800   181837.31  2.463442e+05  \n",
      "3605  -0.6700   170385.22  2.322157e+05  \n",
      "3606   2.2900   205862.49  2.766909e+05  \n",
      "3607   1.6300   117192.08  1.527979e+05  \n",
      "\n",
      "[3608 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "pro = ts.pro_api('7344ede0cb2ff5058820d61132e5fa84a7b19b1cb57149b5c9f9004b')\n",
    "# 拉取数据\n",
    "df = pro.daily(**{\n",
    "    \"ts_code\": \"000021.SZ\",\n",
    "    \"trade_date\": \"\",\n",
    "    \"start_date\": 20100101,\n",
    "    \"end_date\": 20241225,\n",
    "    \"offset\": \"\",\n",
    "    \"limit\": \"\"\n",
    "}, fields=[\n",
    "    \"ts_code\",\n",
    "    \"trade_date\",\n",
    "    \"open\",\n",
    "    \"high\",\n",
    "    \"low\",\n",
    "    \"close\",\n",
    "    \"pre_close\",\n",
    "    \"change\",\n",
    "    \"pct_chg\",\n",
    "    \"vol\",\n",
    "    \"amount\"\n",
    "])\n",
    "print(df)\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
      "0     000021.SZ   20241225  20.26  20.42  19.66  19.76      20.28   -0.52   \n",
      "1     000021.SZ   20241224  20.31  20.50  19.66  20.28      20.27    0.01   \n",
      "2     000021.SZ   20241223  20.88  21.68  20.20  20.27      20.23    0.04   \n",
      "3     000021.SZ   20241220  19.90  20.55  19.70  20.23      20.00    0.23   \n",
      "4     000021.SZ   20241219  19.16  20.07  19.08  20.00      19.44    0.56   \n",
      "...         ...        ...    ...    ...    ...    ...        ...     ...   \n",
      "3603  000021.SZ   20100108  13.50  13.62  13.10  13.46      13.48   -0.02   \n",
      "3604  000021.SZ   20100107  13.34  13.75  13.21  13.48      13.31    0.17   \n",
      "3605  000021.SZ   20100106  13.40  13.86  13.31  13.31      13.40   -0.09   \n",
      "3606  000021.SZ   20100105  13.06  13.70  12.95  13.40      13.10    0.30   \n",
      "3607  000021.SZ   20100104  13.03  13.17  12.85  13.10      12.89    0.21   \n",
      "\n",
      "      pct_chg         vol        amount  \n",
      "0     -2.5641   496775.66  9.929593e+05  \n",
      "1      0.0493   735335.52  1.473577e+06  \n",
      "2      0.1977  1199854.36  2.509186e+06  \n",
      "3      1.1500   779340.05  1.575558e+06  \n",
      "4      2.8807   605595.48  1.195438e+06  \n",
      "...       ...         ...           ...  \n",
      "3603  -0.1500   135241.12  1.814568e+05  \n",
      "3604   1.2800   181837.31  2.463442e+05  \n",
      "3605  -0.6700   170385.22  2.322157e+05  \n",
      "3606   2.2900   205862.49  2.766909e+05  \n",
      "3607   1.6300   117192.08  1.527979e+05  \n",
      "\n",
      "[3608 rows x 11 columns]\n"
     ]
    }
   ],
   "source": [
    "### 存储数据\n",
    "print(df)\n",
    "df.to_csv(\"000021.csv\")\n",
    "stockname='深科技'\n",
    "stockcode='000021'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3598</th>\n",
       "      <th>3599</th>\n",
       "      <th>3600</th>\n",
       "      <th>3601</th>\n",
       "      <th>3602</th>\n",
       "      <th>3603</th>\n",
       "      <th>3604</th>\n",
       "      <th>3605</th>\n",
       "      <th>3606</th>\n",
       "      <th>3607</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ts_code</th>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>...</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "      <td>000021.SZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <td>20241225</td>\n",
       "      <td>20241224</td>\n",
       "      <td>20241223</td>\n",
       "      <td>20241220</td>\n",
       "      <td>20241219</td>\n",
       "      <td>20241218</td>\n",
       "      <td>20241217</td>\n",
       "      <td>20241216</td>\n",
       "      <td>20241213</td>\n",
       "      <td>20241212</td>\n",
       "      <td>...</td>\n",
       "      <td>20100115</td>\n",
       "      <td>20100114</td>\n",
       "      <td>20100113</td>\n",
       "      <td>20100112</td>\n",
       "      <td>20100111</td>\n",
       "      <td>20100108</td>\n",
       "      <td>20100107</td>\n",
       "      <td>20100106</td>\n",
       "      <td>20100105</td>\n",
       "      <td>20100104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>open</th>\n",
       "      <td>20.26</td>\n",
       "      <td>20.31</td>\n",
       "      <td>20.88</td>\n",
       "      <td>19.9</td>\n",
       "      <td>19.16</td>\n",
       "      <td>19.01</td>\n",
       "      <td>19.13</td>\n",
       "      <td>19.68</td>\n",
       "      <td>19.9</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.98</td>\n",
       "      <td>14.17</td>\n",
       "      <td>13.82</td>\n",
       "      <td>13.86</td>\n",
       "      <td>13.53</td>\n",
       "      <td>13.5</td>\n",
       "      <td>13.34</td>\n",
       "      <td>13.4</td>\n",
       "      <td>13.06</td>\n",
       "      <td>13.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>20.42</td>\n",
       "      <td>20.5</td>\n",
       "      <td>21.68</td>\n",
       "      <td>20.55</td>\n",
       "      <td>20.07</td>\n",
       "      <td>19.64</td>\n",
       "      <td>19.4</td>\n",
       "      <td>19.75</td>\n",
       "      <td>20.05</td>\n",
       "      <td>20.3</td>\n",
       "      <td>...</td>\n",
       "      <td>15.25</td>\n",
       "      <td>14.8</td>\n",
       "      <td>14.62</td>\n",
       "      <td>14.3</td>\n",
       "      <td>13.97</td>\n",
       "      <td>13.62</td>\n",
       "      <td>13.75</td>\n",
       "      <td>13.86</td>\n",
       "      <td>13.7</td>\n",
       "      <td>13.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>19.66</td>\n",
       "      <td>19.66</td>\n",
       "      <td>20.2</td>\n",
       "      <td>19.7</td>\n",
       "      <td>19.08</td>\n",
       "      <td>18.86</td>\n",
       "      <td>18.92</td>\n",
       "      <td>19.13</td>\n",
       "      <td>19.54</td>\n",
       "      <td>19.65</td>\n",
       "      <td>...</td>\n",
       "      <td>14.52</td>\n",
       "      <td>14.02</td>\n",
       "      <td>13.78</td>\n",
       "      <td>13.81</td>\n",
       "      <td>13.27</td>\n",
       "      <td>13.1</td>\n",
       "      <td>13.21</td>\n",
       "      <td>13.31</td>\n",
       "      <td>12.95</td>\n",
       "      <td>12.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>close</th>\n",
       "      <td>19.76</td>\n",
       "      <td>20.28</td>\n",
       "      <td>20.27</td>\n",
       "      <td>20.23</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.44</td>\n",
       "      <td>18.94</td>\n",
       "      <td>19.24</td>\n",
       "      <td>19.71</td>\n",
       "      <td>20.09</td>\n",
       "      <td>...</td>\n",
       "      <td>14.74</td>\n",
       "      <td>14.61</td>\n",
       "      <td>14.1</td>\n",
       "      <td>14.19</td>\n",
       "      <td>13.95</td>\n",
       "      <td>13.46</td>\n",
       "      <td>13.48</td>\n",
       "      <td>13.31</td>\n",
       "      <td>13.4</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pre_close</th>\n",
       "      <td>20.28</td>\n",
       "      <td>20.27</td>\n",
       "      <td>20.23</td>\n",
       "      <td>20.0</td>\n",
       "      <td>19.44</td>\n",
       "      <td>18.94</td>\n",
       "      <td>19.24</td>\n",
       "      <td>19.71</td>\n",
       "      <td>20.09</td>\n",
       "      <td>20.0</td>\n",
       "      <td>...</td>\n",
       "      <td>14.61</td>\n",
       "      <td>14.1</td>\n",
       "      <td>14.19</td>\n",
       "      <td>13.95</td>\n",
       "      <td>13.46</td>\n",
       "      <td>13.48</td>\n",
       "      <td>13.31</td>\n",
       "      <td>13.4</td>\n",
       "      <td>13.1</td>\n",
       "      <td>12.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>change</th>\n",
       "      <td>-0.52</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.3</td>\n",
       "      <td>-0.47</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>0.09</td>\n",
       "      <td>...</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.51</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.49</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.09</td>\n",
       "      <td>0.3</td>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct_chg</th>\n",
       "      <td>-2.5641</td>\n",
       "      <td>0.0493</td>\n",
       "      <td>0.1977</td>\n",
       "      <td>1.15</td>\n",
       "      <td>2.8807</td>\n",
       "      <td>2.6399</td>\n",
       "      <td>-1.5593</td>\n",
       "      <td>-2.3846</td>\n",
       "      <td>-1.8915</td>\n",
       "      <td>0.45</td>\n",
       "      <td>...</td>\n",
       "      <td>0.89</td>\n",
       "      <td>3.62</td>\n",
       "      <td>-0.63</td>\n",
       "      <td>1.72</td>\n",
       "      <td>3.64</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>1.28</td>\n",
       "      <td>-0.67</td>\n",
       "      <td>2.29</td>\n",
       "      <td>1.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vol</th>\n",
       "      <td>496775.66</td>\n",
       "      <td>735335.52</td>\n",
       "      <td>1199854.36</td>\n",
       "      <td>779340.05</td>\n",
       "      <td>605595.48</td>\n",
       "      <td>433381.83</td>\n",
       "      <td>308830.78</td>\n",
       "      <td>351595.65</td>\n",
       "      <td>458862.92</td>\n",
       "      <td>509678.7</td>\n",
       "      <td>...</td>\n",
       "      <td>206464.98</td>\n",
       "      <td>219304.3</td>\n",
       "      <td>197513.17</td>\n",
       "      <td>194422.2</td>\n",
       "      <td>202111.19</td>\n",
       "      <td>135241.12</td>\n",
       "      <td>181837.31</td>\n",
       "      <td>170385.22</td>\n",
       "      <td>205862.49</td>\n",
       "      <td>117192.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>992959.319</td>\n",
       "      <td>1473577.218</td>\n",
       "      <td>2509185.609</td>\n",
       "      <td>1575558.383</td>\n",
       "      <td>1195437.518</td>\n",
       "      <td>838621.469</td>\n",
       "      <td>591543.588</td>\n",
       "      <td>680629.054</td>\n",
       "      <td>908393.373</td>\n",
       "      <td>1016898.216</td>\n",
       "      <td>...</td>\n",
       "      <td>305737.6955</td>\n",
       "      <td>318064.251</td>\n",
       "      <td>283448.2528</td>\n",
       "      <td>274355.7503</td>\n",
       "      <td>278292.5148</td>\n",
       "      <td>181456.8307</td>\n",
       "      <td>246344.247</td>\n",
       "      <td>232215.6821</td>\n",
       "      <td>276690.9075</td>\n",
       "      <td>152797.9323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 3608 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0            1            2            3            4     \\\n",
       "ts_code      000021.SZ    000021.SZ    000021.SZ    000021.SZ    000021.SZ   \n",
       "trade_date    20241225     20241224     20241223     20241220     20241219   \n",
       "open             20.26        20.31        20.88         19.9        19.16   \n",
       "high             20.42         20.5        21.68        20.55        20.07   \n",
       "low              19.66        19.66         20.2         19.7        19.08   \n",
       "close            19.76        20.28        20.27        20.23         20.0   \n",
       "pre_close        20.28        20.27        20.23         20.0        19.44   \n",
       "change           -0.52         0.01         0.04         0.23         0.56   \n",
       "pct_chg        -2.5641       0.0493       0.1977         1.15       2.8807   \n",
       "vol          496775.66    735335.52   1199854.36    779340.05    605595.48   \n",
       "amount      992959.319  1473577.218  2509185.609  1575558.383  1195437.518   \n",
       "\n",
       "                  5           6           7           8            9     ...  \\\n",
       "ts_code      000021.SZ   000021.SZ   000021.SZ   000021.SZ    000021.SZ  ...   \n",
       "trade_date    20241218    20241217    20241216    20241213     20241212  ...   \n",
       "open             19.01       19.13       19.68        19.9         20.0  ...   \n",
       "high             19.64        19.4       19.75       20.05         20.3  ...   \n",
       "low              18.86       18.92       19.13       19.54        19.65  ...   \n",
       "close            19.44       18.94       19.24       19.71        20.09  ...   \n",
       "pre_close        18.94       19.24       19.71       20.09         20.0  ...   \n",
       "change             0.5        -0.3       -0.47       -0.38         0.09  ...   \n",
       "pct_chg         2.6399     -1.5593     -2.3846     -1.8915         0.45  ...   \n",
       "vol          433381.83   308830.78   351595.65   458862.92     509678.7  ...   \n",
       "amount      838621.469  591543.588  680629.054  908393.373  1016898.216  ...   \n",
       "\n",
       "                   3598        3599         3600         3601         3602  \\\n",
       "ts_code       000021.SZ   000021.SZ    000021.SZ    000021.SZ    000021.SZ   \n",
       "trade_date     20100115    20100114     20100113     20100112     20100111   \n",
       "open              14.98       14.17        13.82        13.86        13.53   \n",
       "high              15.25        14.8        14.62         14.3        13.97   \n",
       "low               14.52       14.02        13.78        13.81        13.27   \n",
       "close             14.74       14.61         14.1        14.19        13.95   \n",
       "pre_close         14.61        14.1        14.19        13.95        13.46   \n",
       "change             0.13        0.51        -0.09         0.24         0.49   \n",
       "pct_chg            0.89        3.62        -0.63         1.72         3.64   \n",
       "vol           206464.98    219304.3    197513.17     194422.2    202111.19   \n",
       "amount      305737.6955  318064.251  283448.2528  274355.7503  278292.5148   \n",
       "\n",
       "                   3603        3604         3605         3606         3607  \n",
       "ts_code       000021.SZ   000021.SZ    000021.SZ    000021.SZ    000021.SZ  \n",
       "trade_date     20100108    20100107     20100106     20100105     20100104  \n",
       "open               13.5       13.34         13.4        13.06        13.03  \n",
       "high              13.62       13.75        13.86         13.7        13.17  \n",
       "low                13.1       13.21        13.31        12.95        12.85  \n",
       "close             13.46       13.48        13.31         13.4         13.1  \n",
       "pre_close         13.48       13.31         13.4         13.1        12.89  \n",
       "change            -0.02        0.17        -0.09          0.3         0.21  \n",
       "pct_chg           -0.15        1.28        -0.67         2.29         1.63  \n",
       "vol           135241.12   181837.31    170385.22    205862.49    117192.08  \n",
       "amount      181456.8307  246344.247  232215.6821  276690.9075  152797.9323  \n",
       "\n",
       "[11 rows x 3608 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据转置\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#输出图形\n",
    "df[\"close\"].plot(figsize=(16,4),legend=True)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1898e508b00>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#读取数据\n",
    "#coding=utf-8\n",
    "#使用pandas读取数据\n",
    "mydata=pd.read_csv(\"./000021.csv\",parse_dates=[\"trade_date\"],index_col=\"trade_date\")[[\"open\",\"high\",\"low\",\"close\"]]\n",
    "\n",
    "# data=pd.read_csv(\"./stock688333.csv\",parse_dates=[\"trade_date\"])[[\"trade_date\",\"open\",\"high\",\"low\",\"close\",\"vol\"]]\n",
    "\n",
    "#翻转数据\n",
    "\n",
    "mydata=mydata.iloc[::-1]\n",
    "\n",
    "plt.plot(mydata[\"close\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3578 entries, 0 to 3577\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3578 non-null   datetime64[ns]\n",
      " 1   open        3578 non-null   float64       \n",
      " 2   high        3578 non-null   float64       \n",
      " 3   low         3578 non-null   float64       \n",
      " 4   close       3578 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 139.9 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3573</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>19.16</td>\n",
       "      <td>20.07</td>\n",
       "      <td>19.08</td>\n",
       "      <td>20.00</td>\n",
       "      <td>19.466</td>\n",
       "      <td>19.756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3574</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>19.90</td>\n",
       "      <td>20.55</td>\n",
       "      <td>19.70</td>\n",
       "      <td>20.23</td>\n",
       "      <td>19.570</td>\n",
       "      <td>19.759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3575</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>20.88</td>\n",
       "      <td>21.68</td>\n",
       "      <td>20.20</td>\n",
       "      <td>20.27</td>\n",
       "      <td>19.776</td>\n",
       "      <td>19.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3576</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>20.31</td>\n",
       "      <td>20.50</td>\n",
       "      <td>19.66</td>\n",
       "      <td>20.28</td>\n",
       "      <td>20.044</td>\n",
       "      <td>19.820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3577</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>20.26</td>\n",
       "      <td>20.42</td>\n",
       "      <td>19.66</td>\n",
       "      <td>19.76</td>\n",
       "      <td>20.108</td>\n",
       "      <td>19.796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10\n",
       "3573 2024-12-19  19.16  20.07  19.08  20.00  19.466  19.756\n",
       "3574 2024-12-20  19.90  20.55  19.70  20.23  19.570  19.759\n",
       "3575 2024-12-23  20.88  21.68  20.20  20.27  19.776  19.795\n",
       "3576 2024-12-24  20.31  20.50  19.66  20.28  20.044  19.820\n",
       "3577 2024-12-25  20.26  20.42  19.66  19.76  20.108  19.796"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "# import akshare as ak\n",
    "\n",
    "df3 = mydata.reset_index().iloc[30:, :6]  # 取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 计算均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "\n",
    "df3.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "\n",
    "import mplfinance as mpf\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname, fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "plt.xticks(ticks =  np.arange(0,len(df3)), labels = df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy() )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from matplotlib.dates  import date2num\n",
    "#data['date']=date2num(data.index.to_pydatetime())\n",
    "#data=data.sort_values(by=\"date\",ascending=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     trade_date   open   high    low  close\n",
      "0    2010-01-04  13.03  13.17  12.85  13.10\n",
      "1    2010-01-05  13.06  13.70  12.95  13.40\n",
      "2    2010-01-06  13.40  13.86  13.31  13.31\n",
      "3    2010-01-07  13.34  13.75  13.21  13.48\n",
      "4    2010-01-08  13.50  13.62  13.10  13.46\n",
      "...         ...    ...    ...    ...    ...\n",
      "3603 2024-12-19  19.16  20.07  19.08  20.00\n",
      "3604 2024-12-20  19.90  20.55  19.70  20.23\n",
      "3605 2024-12-23  20.88  21.68  20.20  20.27\n",
      "3606 2024-12-24  20.31  20.50  19.66  20.28\n",
      "3607 2024-12-25  20.26  20.42  19.66  19.76\n",
      "\n",
      "[3608 rows x 5 columns]\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3608 entries, 0 to 3607\n",
      "Data columns (total 5 columns):\n",
      " #   Column      Non-Null Count  Dtype         \n",
      "---  ------      --------------  -----         \n",
      " 0   trade_date  3608 non-null   datetime64[ns]\n",
      " 1   open        3608 non-null   float64       \n",
      " 2   high        3608 non-null   float64       \n",
      " 3   low         3608 non-null   float64       \n",
      " 4   close       3608 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(4)\n",
      "memory usage: 141.1 KB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>5</th>\n",
       "      <th>10</th>\n",
       "      <th>30</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-04</td>\n",
       "      <td>13.03</td>\n",
       "      <td>13.17</td>\n",
       "      <td>12.85</td>\n",
       "      <td>13.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-05</td>\n",
       "      <td>13.06</td>\n",
       "      <td>13.70</td>\n",
       "      <td>12.95</td>\n",
       "      <td>13.40</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-06</td>\n",
       "      <td>13.40</td>\n",
       "      <td>13.86</td>\n",
       "      <td>13.31</td>\n",
       "      <td>13.31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-07</td>\n",
       "      <td>13.34</td>\n",
       "      <td>13.75</td>\n",
       "      <td>13.21</td>\n",
       "      <td>13.48</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-08</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13.62</td>\n",
       "      <td>13.10</td>\n",
       "      <td>13.46</td>\n",
       "      <td>13.350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3603</th>\n",
       "      <td>2024-12-19</td>\n",
       "      <td>19.16</td>\n",
       "      <td>20.07</td>\n",
       "      <td>19.08</td>\n",
       "      <td>20.00</td>\n",
       "      <td>19.466</td>\n",
       "      <td>19.756</td>\n",
       "      <td>20.272667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>2024-12-20</td>\n",
       "      <td>19.90</td>\n",
       "      <td>20.55</td>\n",
       "      <td>19.70</td>\n",
       "      <td>20.23</td>\n",
       "      <td>19.570</td>\n",
       "      <td>19.759</td>\n",
       "      <td>20.237667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3605</th>\n",
       "      <td>2024-12-23</td>\n",
       "      <td>20.88</td>\n",
       "      <td>21.68</td>\n",
       "      <td>20.20</td>\n",
       "      <td>20.27</td>\n",
       "      <td>19.776</td>\n",
       "      <td>19.795</td>\n",
       "      <td>20.133000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3606</th>\n",
       "      <td>2024-12-24</td>\n",
       "      <td>20.31</td>\n",
       "      <td>20.50</td>\n",
       "      <td>19.66</td>\n",
       "      <td>20.28</td>\n",
       "      <td>20.044</td>\n",
       "      <td>19.820</td>\n",
       "      <td>20.060000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>2024-12-25</td>\n",
       "      <td>20.26</td>\n",
       "      <td>20.42</td>\n",
       "      <td>19.66</td>\n",
       "      <td>19.76</td>\n",
       "      <td>20.108</td>\n",
       "      <td>19.796</td>\n",
       "      <td>19.987333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3608 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     trade_date   open   high    low  close       5      10         30\n",
       "0    2010-01-04  13.03  13.17  12.85  13.10     NaN     NaN        NaN\n",
       "1    2010-01-05  13.06  13.70  12.95  13.40     NaN     NaN        NaN\n",
       "2    2010-01-06  13.40  13.86  13.31  13.31     NaN     NaN        NaN\n",
       "3    2010-01-07  13.34  13.75  13.21  13.48     NaN     NaN        NaN\n",
       "4    2010-01-08  13.50  13.62  13.10  13.46  13.350     NaN        NaN\n",
       "...         ...    ...    ...    ...    ...     ...     ...        ...\n",
       "3603 2024-12-19  19.16  20.07  19.08  20.00  19.466  19.756  20.272667\n",
       "3604 2024-12-20  19.90  20.55  19.70  20.23  19.570  19.759  20.237667\n",
       "3605 2024-12-23  20.88  21.68  20.20  20.27  19.776  19.795  20.133000\n",
       "3606 2024-12-24  20.31  20.50  19.66  20.28  20.044  19.820  20.060000\n",
       "3607 2024-12-25  20.26  20.42  19.66  19.76  20.108  19.796  19.987333\n",
       "\n",
       "[3608 rows x 8 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 获取股价数据\n",
    "df3 = mydata.reset_index().iloc[:,:]  #取过去30天数据\n",
    "df3 = df3.dropna(how='any').reset_index(drop=True) #去除空值且从零开始编号索引\n",
    "print(df3)\n",
    "df3 = df3.sort_values(by='trade_date', ascending=True)\n",
    "print(df3.info())\n",
    "\n",
    "# 均线数据\n",
    "df3['5'] = df3.close.rolling(5).mean()\n",
    "df3['10'] = df3.close.rolling(10).mean()\n",
    "df3['30']=df3.close.rolling(30).mean()\n",
    "\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "matplotlib.style.use('ggplot') #用于调整图标样式，可选\n",
    "from mplfinance.original_flavor import candlestick2_ohlc\n",
    "from matplotlib.ticker import FormatStrFormatter\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=200)\n",
    "\n",
    "candlestick2_ohlc(ax,\n",
    "                opens = df3[ 'open'].values,\n",
    "                highs = df3['high'].values,\n",
    "                lows = df3[ 'low'].values,\n",
    "                closes = df3['close'].values,\n",
    "                width=0.5, colorup=\"r\",colordown=\"g\")\n",
    "\n",
    "# 显示最高点和最低点\n",
    "ax.text( df3.high.idxmax(), df3.high.max(),   s =df3.high.max(), fontsize=8)\n",
    "ax.text( df3.high.idxmin(), df3.high.min()-2, s = df3.high.min(), fontsize=8)\n",
    "\n",
    "ax.set_facecolor(\"white\")\n",
    "ax.set_title(stockname+\"股价走势\", fontsize=8)\n",
    "\n",
    "# 画均线\n",
    "plt.plot(df3['5'].values, alpha = 0.5, label='MA5')\n",
    "plt.plot(df3['10'].values, alpha = 0.5, label='MA10')\n",
    "plt.plot(df3['30'].values,alpha=0.5, label='MA30')\n",
    "\n",
    "ax.legend(facecolor='white', edgecolor='white', fontsize=6)\n",
    "\n",
    "# 修改x轴坐标\n",
    "tempXticks=np.arange(0,len(df3))\n",
    "#XticksData=data.asfreq(\"2M\").dropna()\n",
    "nameXticks =  df3.trade_date.dt.strftime('%Y-%m-%d').to_numpy()\n",
    "\n",
    "plt.xticks(ticks =tempXticks , labels =nameXticks )\n",
    "plt.xticks(rotation=45, size=8)\n",
    "#修改X轴间隔\n",
    "x_major_locator=plt.MultipleLocator(10)\n",
    "ax.xaxis.set_major_locator(x_major_locator)\n",
    "ax.spines['bottom'].set_color('red')\n",
    "ax.spines['left'].set_color('red')\n",
    "plt.xlim(0,100)\n",
    "# 修改y轴坐标\n",
    "ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data1=pd.DataFrame(data,columns=[\"trade_date\",\"close\"])\n",
    "#data1=pd.DataFrame(data,columns=[\"close\"])\n",
    "data1=mydata\n",
    "data1[\"open\"].plot(label=\"open\")\n",
    "data1[\"close\"].plot(label=\"close\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.items of       trade_date  close\n",
       "0          20.26  20.42\n",
       "1          20.31  20.50\n",
       "2          20.88  21.68\n",
       "3          19.90  20.55\n",
       "4          19.16  20.07\n",
       "...          ...    ...\n",
       "3603       13.50  13.62\n",
       "3604       13.34  13.75\n",
       "3605       13.40  13.86\n",
       "3606       13.06  13.70\n",
       "3607       13.03  13.17\n",
       "\n",
       "[3608 rows x 2 columns]>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd=[]\n",
    "for item in data1.items():\n",
    "    ddd.append(item)\n",
    "ind=ddd[0][1].values\n",
    "da1=ddd[1][1].values\n",
    "index1=[]\n",
    "data2=[]\n",
    "for item in ind:\n",
    "    index1.append(item)\n",
    "index1.reverse()\n",
    "for item in da1:\n",
    "    data2.append(item)\n",
    "data2.reverse()\n",
    "data1={'trade_date':index1,'close':data2}\n",
    "stockdata=pd.DataFrame(data1)\n",
    "\n",
    "stockdata.items    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[13.1 ],\n",
       "       [13.4 ],\n",
       "       [13.31],\n",
       "       ...,\n",
       "       [20.27],\n",
       "       [20.28],\n",
       "       [19.76]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数据检查\n",
    "df3.iloc[:,4:5].values[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.dates  import date2num\n",
    "date2num(df3.trade_date.values)\n",
    "#添加data数据列\n",
    "df3['date']=date2num(df3.trade_date.values)\n",
    "#df3=df3.sort_values(by=\"date\",ascending=True)\n",
    "#df3=df3[['date','open', 'high', 'low', 'close']]\n",
    "\n",
    "ind=np.arange(0,2677)\n",
    "dataf1=df3[['date','open', 'high', 'low', 'close']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14613.0</th>\n",
       "      <td>13.03</td>\n",
       "      <td>13.17</td>\n",
       "      <td>12.85</td>\n",
       "      <td>13.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14614.0</th>\n",
       "      <td>13.06</td>\n",
       "      <td>13.70</td>\n",
       "      <td>12.95</td>\n",
       "      <td>13.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14615.0</th>\n",
       "      <td>13.40</td>\n",
       "      <td>13.86</td>\n",
       "      <td>13.31</td>\n",
       "      <td>13.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14616.0</th>\n",
       "      <td>13.34</td>\n",
       "      <td>13.75</td>\n",
       "      <td>13.21</td>\n",
       "      <td>13.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14617.0</th>\n",
       "      <td>13.50</td>\n",
       "      <td>13.62</td>\n",
       "      <td>13.10</td>\n",
       "      <td>13.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20076.0</th>\n",
       "      <td>19.16</td>\n",
       "      <td>20.07</td>\n",
       "      <td>19.08</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20077.0</th>\n",
       "      <td>19.90</td>\n",
       "      <td>20.55</td>\n",
       "      <td>19.70</td>\n",
       "      <td>20.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20080.0</th>\n",
       "      <td>20.88</td>\n",
       "      <td>21.68</td>\n",
       "      <td>20.20</td>\n",
       "      <td>20.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20081.0</th>\n",
       "      <td>20.31</td>\n",
       "      <td>20.50</td>\n",
       "      <td>19.66</td>\n",
       "      <td>20.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20082.0</th>\n",
       "      <td>20.26</td>\n",
       "      <td>20.42</td>\n",
       "      <td>19.66</td>\n",
       "      <td>19.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3608 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          open   high    low  close\n",
       "date                               \n",
       "14613.0  13.03  13.17  12.85  13.10\n",
       "14614.0  13.06  13.70  12.95  13.40\n",
       "14615.0  13.40  13.86  13.31  13.31\n",
       "14616.0  13.34  13.75  13.21  13.48\n",
       "14617.0  13.50  13.62  13.10  13.46\n",
       "...        ...    ...    ...    ...\n",
       "20076.0  19.16  20.07  19.08  20.00\n",
       "20077.0  19.90  20.55  19.70  20.23\n",
       "20080.0  20.88  21.68  20.20  20.27\n",
       "20081.0  20.31  20.50  19.66  20.28\n",
       "20082.0  20.26  20.42  19.66  19.76\n",
       "\n",
       "[3608 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1.set_index(\"date\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 深度学习拟合股票"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确保结果尽可能重现\n",
    "\n",
    "seed(1)\n",
    "tf.random.set_seed(1)\n",
    "\n",
    "# 设置相关参数\n",
    "n_timestamp  = 5    # 时间戳\n",
    "n_epochs     = 30    # 训练轮数\n",
    "# ====================================\n",
    "#      选择模型：\n",
    "#            1: 单层 LSTM\n",
    "#            2: 多层 LSTM\n",
    "#            3: 双向 LSTM\n",
    "# ====================================\n",
    "model_type = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14613.0</td>\n",
       "      <td>13.03</td>\n",
       "      <td>13.17</td>\n",
       "      <td>12.85</td>\n",
       "      <td>13.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14614.0</td>\n",
       "      <td>13.06</td>\n",
       "      <td>13.70</td>\n",
       "      <td>12.95</td>\n",
       "      <td>13.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14615.0</td>\n",
       "      <td>13.40</td>\n",
       "      <td>13.86</td>\n",
       "      <td>13.31</td>\n",
       "      <td>13.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14616.0</td>\n",
       "      <td>13.34</td>\n",
       "      <td>13.75</td>\n",
       "      <td>13.21</td>\n",
       "      <td>13.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14617.0</td>\n",
       "      <td>13.50</td>\n",
       "      <td>13.62</td>\n",
       "      <td>13.10</td>\n",
       "      <td>13.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3603</th>\n",
       "      <td>20076.0</td>\n",
       "      <td>19.16</td>\n",
       "      <td>20.07</td>\n",
       "      <td>19.08</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3604</th>\n",
       "      <td>20077.0</td>\n",
       "      <td>19.90</td>\n",
       "      <td>20.55</td>\n",
       "      <td>19.70</td>\n",
       "      <td>20.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3605</th>\n",
       "      <td>20080.0</td>\n",
       "      <td>20.88</td>\n",
       "      <td>21.68</td>\n",
       "      <td>20.20</td>\n",
       "      <td>20.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3606</th>\n",
       "      <td>20081.0</td>\n",
       "      <td>20.31</td>\n",
       "      <td>20.50</td>\n",
       "      <td>19.66</td>\n",
       "      <td>20.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3607</th>\n",
       "      <td>20082.0</td>\n",
       "      <td>20.26</td>\n",
       "      <td>20.42</td>\n",
       "      <td>19.66</td>\n",
       "      <td>19.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3608 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         date   open   high    low  close\n",
       "0     14613.0  13.03  13.17  12.85  13.10\n",
       "1     14614.0  13.06  13.70  12.95  13.40\n",
       "2     14615.0  13.40  13.86  13.31  13.31\n",
       "3     14616.0  13.34  13.75  13.21  13.48\n",
       "4     14617.0  13.50  13.62  13.10  13.46\n",
       "...       ...    ...    ...    ...    ...\n",
       "3603  20076.0  19.16  20.07  19.08  20.00\n",
       "3604  20077.0  19.90  20.55  19.70  20.23\n",
       "3605  20080.0  20.88  21.68  20.20  20.27\n",
       "3606  20081.0  20.31  20.50  19.66  20.28\n",
       "3607  20082.0  20.26  20.42  19.66  19.76\n",
       "\n",
       "[3608 rows x 5 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataf1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拟合开始，数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x18991523f20>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#\n",
    "#拟合开始\n",
    "#\n",
    "training_set = dataf1.iloc[0:2048, 4:5].to_numpy()#要提取一列数据，否则会出错\n",
    "test_set     = dataf1.iloc[3626 - 300:, 4:5].to_numpy()\n",
    "#print(training_set)\n",
    "plt.plot(training_set)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据归一化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#将数据归一化，范围是0到1\n",
    "sc  = MinMaxScaler(feature_range=(0, 1))\n",
    "training_set_scaled = sc.fit_transform(training_set)\n",
    "testing_set_scaled  = sc.transform(test_set) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取前 n_timestamp 天的数据为 X；n_timestamp+1天数据为 Y。\n",
    "def data_split(sequence, n_timestamp):\n",
    "    X = []\n",
    "    y = []\n",
    "    for i in range(len(sequence)):\n",
    "        end_ix = i + n_timestamp\n",
    "        \n",
    "        if end_ix > len(sequence)-1:\n",
    "            break\n",
    "            \n",
    "        seq_x, seq_y = sequence[i:end_ix], sequence[end_ix]\n",
    "        X.append(seq_x)\n",
    "        y.append(seq_y)\n",
    "    return array(X), array(y)\n",
    "\n",
    "X_train, y_train = data_split(training_set_scaled, n_timestamp)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练数据重整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train          = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "\n",
    "X_test, y_test   = data_split(testing_set_scaled, n_timestamp)\n",
    "X_test           = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构建模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\76496\\AppData\\Roaming\\Python\\Python312\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:200: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#五、构建模型\n",
    "# 建构 LSTM模型\n",
    "model_type=2\n",
    "if model_type == 1:\n",
    "    # 单层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu',\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(units=1))\n",
    "if model_type == 2:\n",
    "    # 多层 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(LSTM(units=50, activation='relu', return_sequences=True,\n",
    "                   input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(LSTM(units=50, activation='relu'))\n",
    "    model.add(Dense(1))\n",
    "if model_type == 3:\n",
    "    # 双向 LSTM\n",
    "    model = Sequential()\n",
    "    model.add(Bidirectional(LSTM(50, activation='relu'),\n",
    "                            input_shape=(X_train.shape[1], 1)))\n",
    "    model.add(Dense(1))\n",
    "\n",
    "model.summary()  # 输出模型结构"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型编译"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "              loss='mean_squared_error')  # 损失函数用均方误差\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 16ms/step - loss: 0.1150 - val_loss: 0.1322\n",
      "Epoch 2/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0162 - val_loss: 0.0124\n",
      "Epoch 3/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0036 - val_loss: 0.0284\n",
      "Epoch 4/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0020 - val_loss: 0.0134\n",
      "Epoch 5/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0015 - val_loss: 0.0075\n",
      "Epoch 6/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0013 - val_loss: 0.0056\n",
      "Epoch 7/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0013 - val_loss: 0.0051\n",
      "Epoch 8/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0013 - val_loss: 0.0048\n",
      "Epoch 9/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0012 - val_loss: 0.0046\n",
      "Epoch 10/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0012 - val_loss: 0.0045\n",
      "Epoch 11/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0012 - val_loss: 0.0043\n",
      "Epoch 12/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0012 - val_loss: 0.0042\n",
      "Epoch 13/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 0.0040\n",
      "Epoch 14/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 0.0039\n",
      "Epoch 15/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 0.0038\n",
      "Epoch 16/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 0.0011 - val_loss: 0.0036\n",
      "Epoch 17/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 0.0033\n",
      "Epoch 18/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0011 - val_loss: 0.0032\n",
      "Epoch 19/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 0.0031\n",
      "Epoch 20/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 0.0010 - val_loss: 0.0030\n",
      "Epoch 21/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.8301e-04 - val_loss: 0.0028\n",
      "Epoch 22/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 9.5844e-04 - val_loss: 0.0027\n",
      "Epoch 23/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 9.3569e-04 - val_loss: 0.0026\n",
      "Epoch 24/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 9.0921e-04 - val_loss: 0.0025\n",
      "Epoch 25/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 8.8108e-04 - val_loss: 0.0024\n",
      "Epoch 26/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 8.4874e-04 - val_loss: 0.0023\n",
      "Epoch 27/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 8.1414e-04 - val_loss: 0.0024\n",
      "Epoch 28/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.7694e-04 - val_loss: 0.0026\n",
      "Epoch 29/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.4426e-04 - val_loss: 0.0029\n",
      "Epoch 30/30\n",
      "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 7.1955e-04 - val_loss: 0.0032\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)          │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m5\u001b[0m, \u001b[38;5;34m50\u001b[0m)          │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">91,955</span> (359.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m91,955\u001b[0m (359.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">30,651</span> (119.73 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m30,651\u001b[0m (119.73 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">61,304</span> (239.47 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m61,304\u001b[0m (239.47 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#七、训练模型\n",
    "history = model.fit(X_train, y_train,\n",
    "                    batch_size=64,\n",
    "                    epochs=n_epochs,\n",
    "                    validation_data=(X_test, y_test),\n",
    "                    validation_freq=1)  # 测试的epoch间隔数\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出训练结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "#plt.title('Training and Validation Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 29ms/step\n"
     ]
    }
   ],
   "source": [
    "predicted_stock_price = model.predict(\n",
    "    X_test)                        # 测试集输入模型进行预测\n",
    "predicted_stock_price = sc.inverse_transform(\n",
    "    predicted_stock_price)  # 对预测数据还原---从（0，1）反归一化到原始范围\n",
    "real_stock_price = sc.inverse_transform(y_test)  # 对真实数据还原---从（0，1）反归一化到原始范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gtm7dyrfffstZZ53F9OnTqaqqCunYsWPH8vnnn7fyCAVBEISOjoiXDsDOnTspKSlh2rRpZGZmcuedd7bbQkYmk4n4+Hi6d+/OH//4R8rLy9myZUtIx1osFmJjY1t5hIIgCC3E7YZGCqgKrYuIF1VFqawM+R8VFU3av8FzhZgO1717dxRFYdasWbhcLoYPH86cOXMAuOqqq8jMzOTgwYNcdtllZGZm8sILLxjHfvfdd5xzzjkMHTqUW265hZKSEmPbqlWrmDhxIgMHDuTqq6/m0KFDda5dWVnJ+eefz6xZs5o1vWaz2ai3c+mll/LOO+/w8ssvM2rUKJYtWxawb31uo4bG+dVXX3H22WczZMgQ7r77bhwOR7PGKQiCEBJeLynnnUfKxIng8bT1aLosbV7npa1RqqrIGDCgTa6du3MnagiF+JKTk3n++ef505/+xLvvvsvdd9/NZZddBsArr7yCy+Vi4sSJPPHEE4wdOxa73Q5o/aOuueYaHnroIU455RRmzpzJnXfeydy5czlw4ADXX389jz/+OKeeeiqPPfYY999/P3PnzjWu6/F4uPnmmxk+fDh33XVXk16b1+s1Kh8PHTrUWP/f//6XxMREZs2aFbC+Phoa5759+5g+fTpPPvkk48eP58Ybb2T27NnccccdTRqrIAhCqCglJVi3bTP+VpOS2nhEXZMuL146ClOmTOG0007jlVde4c9//jNbtmzhoYceMqoQm0wmoqOjiY+PN45ZtGgRo0eP5qqrrgLgqaee4sQTTyQ/P5/Fixczfvx4pk2bBsD9999fx73z4IMPsnr1an7++eeQx/nLL78wZMgQqqqqSExM5F//+ldAplhlZSWLFi0iKioqpOqwDY1zyZIlDBs2jMsvvxyAa665hrffflvEiyAIrYaptNT4W3E6kXJybUOXFy9qZCS5O3eGvL/FYgnadqC51w6FvLw8qqur6d27N3fddRcnnXQSl112WUBxv2AcOnSInj17Gsvp6enYbDYOHTpEbm5uwLHdu3cP6OCdk5PDhg0bGDlyJPPnzw+5QGC/fv148803sdlsdTLHQBMYTSlF39A48/Ly2Lx5M0OGDAG0HlnR0dEhn1sQBKGpmPxc74rEvbQZXV68oCghuW4MrFbUY9yT53//+x/Lli0z3DDjx4/HarVS6vcEYDKZ6pSUzszM5NtvvzWWc3NzcTgcZGZm0r17d7777jtj2+7du7n55pv59NNPAYiLi+PNN98kOzuba6+9lmnTpoUUTGu1WunRo0e925vaBLChcWZkZHDOOefw4IMPApqbK9TMJkEQhOagFBfX/C3ipc2QgN0OwKmnnsratWtZsmQJubm5zJo1i9TUVPr372/s07t3b1asWMHhw4dZtWoVABdffDE//fQT8+fPJzs7m7/85S+cd955pKSkcNFFF/H999/zzjvvkJOTw3PPPUdycrJRLj42NpakpCROOOEETjzxRF588cU2ee0NjfOiiy7ihx9+YO/evQC8+uqr/PGPf2yTcQqC0DXwdxsRQoKAZfNmEm++GfO+fa03qC6IiJcOwJAhQ3j22Wd55plnOO2001i9ejVz584lIiLC2OeBBx7gq6++4qSTTuIf//gHoFkt5s2bx+uvv86kSZOIiori2WefBaBHjx7MnTuXV155hbPOOovS0lJjW23uvvtuXn31VQ4fPtz6L7YWDY2zV69e/POf/+Thhx/mzDPP5JdffuHf//73MR+jIAhdh6a6jaLnzyfyf/8jymc5F8KDonbS9pUFBQVBA0JLS0tb1GrAarWGFGgqNIzMY8sJ1xy29DvRkVEUhYyMDHJzc6WTbwvoSvMY8+KLxD3+OABHFi3C2UhV8ITbbyfq/fepuO46SnzHBaMrzWF9WK1WUlJSQtpXLC+CIAiCECKKn+UlFLeR4tvHP1ZGaDkiXgRBEAQhRJqcbeQTL/7HCS1HxIsgCIIghEhTxYu+j0ksL2FFxIsgCIIghIhSq0hdo/vrlhcRL2FFxIsgCIIghEiA+ycU8VJdrf0vbqOwIuJFEARBEELE1MQidYbbqKQk5Ga8QuOIeBEEQRCEEGmq20gP2FU8HpTy8tYaVpdDxIsgCIIghIKqNt1t5JdOLXEv4UPESwdg9erVZGZmkpmZSa9evTj77LNZsWJF2K8xrlaxpWDrjgXvvPMOl156acj7jh07lmHDhnHPPffgbMVeI7NmzWpWx+o77rjDeP8GDx7MjBkzKCgoaPXrCoIQXpTKShS/xrxKKHVe/H6TJO4lfIh46SDExsaydetWfvjhB6ZPn86NN95IXl5eq15z7NixfP75500+bty4caxevboVRhTIxo0beeSRR3jxxRdZtGgR3377LfPmzTO2HzhwgMzMzFYfRyhcc801bN26lY8//hiv18uf//znkI+95ZZbePLJJ1txdIIghEJt8dGUbCMQy0s4EfHSQVAUhfj4eNLS0rjqqqvo0aNHQLfl1sBisYTUSbqtWLNmDccddxyjR49m0KBB3H777QGdttsTERERxMfH069fP+644w5WrFiB1+sN6Vi73d7kbtyCIISfOoXmQrH0+rKNQMRLOOny4kVVobJSCflfRUXT9m/oX0sCz81ms9HX5o477mDWrFksXLiQU089lddee83Yb8OGDUyZMsVwV/jf3N966y1OPPFERo0aFdQNVZ/baNWqVUycOJGBAwdy9dVXc+jQIQCuuuoqMjMzOXjwIJdddhmZmZm88MILxnFfffUVZ599NkOGDOHOO+/E4fdE8o9//IMRI0YwYcIENm/eHNIc9O3blx9++IFly5YB8Otf/9roKt23b1/Gjx8PYLhs1q5daxz7+uuvM27cOEaNGsWsWbMChMQrr7zCmDFjGDZsGH/+85+D9g/atWsXxx9/PF9//XVIY/XHbDbj8XiMa2ZmZrJ9+3buuecehg0bVkeA1ec2evnll4OOU1VVZs+ezdixYxk5ciRz5sxp8hgFQahLbfHSqNtIVQOsM1JlN3xY2noAbU1VlcKAARltcu2dO3OJimq6glm5ciW7d+9mzJgxxroVK1agKAozZ85k6NChAJSUlHD11Vfz29/+lpdffpl77rmHRx55hGeeeYYtW7Zw//33M3v2bHr27Mn06dNDuvaBAwe4/vrrefzxxzn11FN57LHHuP/++43Ozy6Xi4kTJ/LEE08wduxY7HY7APv27WP69Ok8+eSTjB8/nt/97nfMnj2bO+64g2XLljFnzhxeffVVzGYz1113HUOGDGl0LOeeey4zZsxgxowZjBw5kgceeIDRo0cDmmg7ePAg55xzDlu3bgUgJiYGgI8//phnn32Wl156idjYWG666Sbi4uK44YYb+OCDD/j3v//Nyy+/TLdu3ZgxYwavv/46N9xwg3HdgoICrr32Wh544AFOP/30kOZNp7q6mjfeeIMxY8ZgsdR8/e6++27Gjh3LnDlziIqKavQ8H3zwAc8//3zQcS5cuJAXXniBN954A4Arr7yS4447jrFjxzZprIIgBFJHvDRmeXG7UfwejMTyEj66vHjpKJSWljJkyBAcDgc2m40nnniC3r17G9uzs7NZtWpVQHfgzz//HKvVyh133IGiKNxwww3cdtttAHz22WeceuqpTJo0CYCbbrqJF198sdFxLF68mPHjxzNt2jQA7r//frZs2QJg3HRNJhPR0dHEx8cbxy1ZsoRhw4Zx+eWXA/Cb3/yG+fPnc8cdd/DJJ58wdepUw1Jy5ZVXsn79+pDm5S9/+QtXXHEFs2bN4pJLLmH27NlMnjyZuLg4w+XlPw6A+fPnM2PGDE4++WRAEw7/+Mc/uOGGG3j77be54YYbDIvTv/71L9x+AXpVVVVcd911ZGZmhhxUDPDmm2/y3nvvUV5ezvDhw+vM9ZAhQ3jggQdCPt/bb7/N7373u6DjfO+997jqqqsMITdx4kSWLVsm4kUQWkhTY15qW2YkYDd8dHnxEhmpsnNnbsj7WyyWgJtZS68dKjExMSxbtgyLxUJ6ejqKogRsv/TSSwOEC0BeXh6FhYWGJcbr9VJeXk51dTWHDx+me/fuxr69evUKaRy5ublkZWUZy927dw84T33k5eWxefNmw6LidruJjo4GID8/nwkTJhj79uzZMyTxsmvXLhISEujduzf/+te/6N27N48++iiTJ09u8LhDhw4FvN6ePXsarq/ar2/EiBEBx3788cdcfvnlLF++nM2bNzN8+PBGxwkwdepU7rzzTuLi4uqIKYDf/va3IZ1HJzc3l549ewYdZ15eHj/99BNvvvkmAA6HwxCpgiA0n6bGvNQWN2J5CR9dXrwoCk1y3Vit4HId+yqJJpOJHj161Ls9mKshIyOD4447jtmzZwNaLERpaSlWq5Xk5GS2bdtm7JuTkxPSOLp37x4QKLx7925uvvlmPv30U0wmkzFWtVZAT0ZGBueccw4PPvggoAUgl5WVAdCtW7eAzKlQx/L4448zYsQII87llFNOYe7cucZ2fTyqqgaIvczMTPbv328s79+/38hK6t69OwcOHDC2LVq0iNWrV/PMM88AWgbW3/72N/7zn//wxBNP8NZbb4U01piYmCa/fw3RvXt3srOzg44zIyODK664ggsuuADQxEtERESTzi8IQl108aIqCoqqolY34jbyC9YFES/hpMsH7HZmzj77bHJycli/fj0mk4kPPviAq6++GlVVmTRpEl9//TVffPEF27dvNwROY1x00UV8//33vPPOO+Tk5PDcc8+RnJxsCAWA3r17s2LFCg4fPsyqVauM43744Qf27t0LaEGxuuiYNGkSixcv5scff2TdunUhC4LTTz+d+fPns3btWrKzs3nhhRcCYlBSU1OJiopi+fLlHDx40AjYveqqq5gzZw5r1qxh8+bNzJo1i2uuuQaAadOmMWfOHH744Qd27drF7Nmz61hpTCYTv/nNb9i2bZvx+o4106ZN4+WXXw46zssuu4wlS5ZQXl5OVVUV9957L6+//nqbjFMQOhN6dV1vcjIbOJ6s5Qt46aXo+vev5TaSgN3w0eUtL52Z+Ph4XnvtNe6//35++eUXBg0axGuvvYbFYuG4447jwQcf5J577sFsNnPeeefx2WefNXrOHj16MHfuXB555BEefPBBTjrpJJ599tmAfR544AFuu+02Xn31VU444QROPfVUevXqxT//+U8efvhh9u/fz4knnsi///1vACZPnsy2bduYPn06iYmJTJo0yRA5DXHNNddw6NAhZsyYgcPh4IwzzuCxxx4ztlutVp555hn+8pe/UFJSwvTp0znxxBOZPHkyhw8f5vbbb8flcnH11VcbAcsXXnghhw8f5ve//z0Oh4OpU6dy00031bl2ZGQkt956K0888QRLly6t48ZrbS688EKOHDkSdJwXX3wxhw8f5tprr6W8vJxJkybxpz/96ZiOTxA6I7rlxJuczMqC0yh2x/LRR05uuqki6P613UaKWF7ChqLWtu93EgoKCoKmuJaWltaJDWkKVqs16HmFpiHz2HLCNYct/U50ZBRFISMjg9zc3DquTiF0uso8Jt5wA5FLl+I4+WSeWj2Rh3iYuDgvW7fmEez5xfrzz6Scf76x7O7Rg/x66nN1lTlsCKvVSkpKSkj7ittIEARBEEJA8cWweOPiKCYBgNJSE0eOBL+V6m4j1adsJOYlfIh4EQRBEIQQMMRIbCxFJBrrd+2qJwLDt783ORkAU1kZeDytO8gugogXQRAEQQgBXbz4W16gfvFi7O/nCpFaL+FBxIsgCIIghEITLS+GeImOxhsXhwqsW+GkuPjYBvh3RrqkeAm1IZ4gdHbkuyAIoWOIkVriZc+eesSLnm1ks+HJzORbJjDlD6P4058SWnuonZ4uJ16ioqIoKyuTH22hy+P1eikrK2tygTxB6Kr4x7yE4jYyLDUREXgyM9nJAAC2bLG26ji7Al2uzovFYiE6Opry8vJmHR8REYEzlDboQoPIPLaccMxhdHR0QINIQRDqpz7Ly4EDZqqqIDKy1v6+7CTVbsebmmocc+iQGa8XTF3OfBA+uuSvlsViaVZdC8nDDw8yjy1H5lAQjj26GHFFx1OO1vjValVxuRT27rUwdGhg3zvdbaTabLizsihGs7i4XAqHD5vIyBAPQHMR3ScIgiAIoeCzvBSZkoxVgwdrhSKzs+vaAoz2ABEReLp3D7DWHDxobsWBdn5EvAiCIAhCY6iqIUZKfPEuMZQRH6fVbamurptBZMTI2Gx4srICxEtOTpd0fIQNES+CIAiC0BhuN4ov0aPIGw9AAsVEmLV1QcPP/NxGnqysgCDfnByxvLSENpV+P/74I2+88QZHjhyhb9++/P73vycrK6ve9YIgCILQFvh3iC72aPEuiRRhNWtCxukMYnnRA3ZtNrwpKRQpVeALURO3UctoM8tLXl4eL774IldeeSUvvfQSycnJvPzyy/WuFwRBEIS2IkC8VGtpRQkUY7NoQbrBeqT6u40wmSi2JBvbRLy0jDYTLzk5OVxxxRWcfPLJJCQkcO6557J79+561wuCIAhCm+FnRSkp0W6diRQRYdJiXoJaXvyK1AEUKTWBvuI2ahlt5jY68cQTA5YPHTpEenp6vevrw+Vy4fKTvIqiEOlLtleC9ShvAfr5wn3erobMY8uROQwPMo/hoSvMo8kvfqW0tEa8uMyaeHG5lDqv39/yoigKRZ6aEh2a5UVBP6QrzGE4aRfhzm63mw8//JALLrggpPX+LF68mPfff99Y7tOnD08//TQpfo2wwk1DYkoIHZnHliNzGB5kHsNDp57HI0cAMNnteHwiJIFinNHabdRmiyMjo1b9MJ8QiU9NxZaYQbVfQ+mKChN2ewZJSYGHdOo5DCPtQry8/fbb2O12Jk6cGNJ6f6ZOncqUKVOMZV21FhQU4Ha76zusWSiKQnp6Onl5eVIYrAXIPLYcmcPwIPMYHrrCPFpzckgG3FYrhw5VANEkUsQhZxmQzNGj5eTmlgUck1haih0orq5m//bDQBoKXrpZSzniSmDt2gKGD9fuU11hDhvDYrGEbHhoc/Hy888/s3z5ch5//PGAMuX1ra+N1WrFag3eJ6K1PgCqqnbZD1c4kXlsOTKH4UHmMTx06nn0xbxgs1FcrLmNEijmiKKFLTidde85eraR12ajpMRnhaGEXuYcjrgSOHjQzLBhgZG+nXoOw0ib1nk5fPgwzz//PDNmzAhIha5vvSAIgiC0CX4Bu6WlmhBJpAgrNeKlNv4VdouLa47pziEA8vOl1FpzaTPLi9Pp5KmnnmLMmDGMGTOGat8Hw2QyBV1v8wU8CYIgCMKxxgi+tduNbKMEirGZNNUSLNvIv0idf4ZSpLu0/mOEkGgz8bJhwwZycnLIycnhiy++MNb/5je/Cbr+hRdeIDU1tS2GKgiCIHRx/DOHSg77pUr7LC8uV8PtAUrzawRPpLscAIdDxEtzaTPxMnbsWN59992g2xrKLhIEQRCEY02AePHFryRQTISiWVcaLFIXEWEck0gRdjSPgl/dO6GJiMNNEARBEBpBFyLeiFpF6lRNvASzohgxL36upnhrBTYc9R4jhIaIF0EQBEFoDJ8QqbTE4fHUZA7pQiSY5YUAa43PbWSr8rO8iHhpLiJeBEEQBKERdCtKqTlBW8ZLDOVEoLuNGoh58XMbJUQ5RLyEAREvgiAIgtAIes2WMkWrohtjqUYBItT6XUBKkJYC8TFuw1oTLL1aCA0RL4IgCILQCLoVpcwUD0C0VVuO8Gqipo7byONB8a1U/Qrbxcd5xPISBkS8CIIgCEIjGOKFWABirb4aZKouXmo1ZfQ3q/gVtotPxBAv1dUiXpqLiBdBEARBaITa4iUmQhMnNp/lpU7BOb88aP+A3dhERbKNwoCIF0EQBEFoDJ8YKVdjAIixBbqNasevGMG6ZjNYLEbMS0KKxbC8SIXd5iPiRRAEQRAawbC8eKMBiLFp8Sw1MS9K0P3ViAhUFcrKtO3RyRFSpC4MiHgRBEEQhEYwso08UQDE2DXxYvNUAnWtKEbMi81GdTV4vdr2qNRIcRuFAREvgiAIgtAIhuVFFy+RbqB+t5HRhdpup6Ki5lZrT4kWt1EYEPEiCIIgCI2gi5cKtx2AaJ94sbkqgCBuo6oqQHMbVVT4rC5RXpSkBMk2CgMiXgRBEAShMfSAXZ94iYn2AmBzaR2ia1teLAcPAuDp3t0QLzExKt6EBIl5CQMiXgRBEAShEQy3kdMnXuK126e9ogioa3mx7NkDgLtvX8NtFBWl4o2Pr6mw25B4UdWwjb0zIuJFEARBEBpBFy/lThsAUQlmAGw+8eLxKHg8NfubA8SLL9MoWkWNjcVm0oJ96wvYtX39NWknnIBt2bLwv5BOgogXQRAEQWgEQ7w4IgCISbICYC8rNPbxdx3plhePn3iJifGCohARqx1br3j58kvMR45g/+qr8L6IToSIF0EQBEFoBD1VutyhCY+obpqIsZfXiBfDdaSqAW6j8vIaywtARJxmvXG6TEG9Q6ajR7VrVlaG+VV0HkS8CIIgCEJj+Mwq5dWaeIlO0WJfbJXFfrtoIsWUn4+pvBzVZMLdsyeVlXq2kU+8JEYaxwQL2jUVaa4oES/1I+JFEARBEBrBcBtVWQCI8okXBbBatMwj3W1kuIx69ACbzQjYjYnR9otIsBvnDeY6EstL44h4EQRBEIRGMMRLpRaoG5NgxhupWVCsVs2ioruNDJdRv37aMbXcRubkeOO8wQrViXhpHEtbD0AQBEEQ2ivmAwcwFRSguN14Uais0sRLdLSKGhcHVVXYLB4qMdcVL336ANRxG3nT07BTRTWRDVpeTBUVrfviOjAiXgRBEAShHlJPOQXFrVXTLSfGWB8To+KNi8N8+DBWs5Yjrcev+KdJAwFF6gC8qanYqaaaSL2LQA3V1YZoEctL/YjbSBAEQRDqQRcuAGXEAmAyqdjtWs0WgAiTJl5cLgVUFesvvwA14qW8XLvVRvuq8npSU2sK1dVyG+nBuiDipSFEvAiCIAhCMGrlMZdakgDNgqIo4I3XYlcifEXnXC4Fy44dWLKzUSMicI0cCQRxG6Wl+bUIqCVefC4jEPHSEOI2EgRBEIRguFwBi6XWJHDXBN7qlhe9Yq7rSBn2bz8BwHHqqcb22m4jT6jipaJCE1CKNHCsjYgXQRAEQQiCUisgpdwUB9SkPHvjtOUIj9ZBOuq2u4lKXQdA1eTJNccZbqMay4sNLa7FWVKNvxMkQLx4vVogjb0mtToYpsOHwWrFm5TUtBfYgRG3kSAIgiAEobZ4qRN46xMvtqpSANyVLiz79qGaTDjOPbfOcYbFJioKu0krCuPKKw64hr94ATA14jpSqqpIPesskidNAq835NfW0RHxIgiCIAhBUGqVv9UDdg0RoouX6hIAnGgtA5zjxwdYQWpiXmrEhS3CV9juSFnANWqLl8biXsx792IqLsZy6BCmvLwQXlXnQMSLIAiCIAShtuVFFy+G20iPefFqbqPKbpk4xo6l7PbbA47Ti9TpFhsAm9beCNeRwFouTRYvBw8af1v27Wtw386EiBdBEARBCEZjlhc92wjNBVRxwlgKFy/GecopxjFeL1RWBsa8AEREaoLGdbQR8dJIoTpzTo7xt2X//kZeUOdBAnYFQRAEIQhKlWZRcWdmUjV1KofzpsP7fjEvep0Xn3ipjkuuc46qqppMIX/xYovSBI2zqCpg/6ZaXix+4sUslhdBEARB6NrobiM1Joayv/yFkoQeQI3bSLe86AXnqmO61TmH7jLSC9vpRERrtgNHSaB1p8mWF3EbCYIgCIKgowfsqr5U5dpZQ7UtL46YuqnK/sf4l2uJiLEC4CoNjKvRxYs3RmtFoFt/6sNfvIjlRRAEQRC6OIblxSde9Hot9bmNHJHxtU9hZBr5u4wAIuK0zCRHWU37AVTVaA/g6aFZeRprzlgn5qVWVeDOiogXQRAEQQiCIV58qUE1dV4C3Ua6eNFTpf2p3ddIx5agCSJnuZ94qaw0runJzNTG0FDMS3U15vx8Y9FUVlbH7dRZEfEiCIIgCEGo7TbS41eMbKPoaFSTya/JYt1z1HY16VgTo7RjXApKeTmR778Pxx+vnddmw5OSoo2hAcuL+dAhALyRkXgyMrR1XcR1JOJFEARBEIJg1Hmpx22EoqDGxdVYXpx1exDVJ14iYrWYl2rsWPbsIea552D3bm3d2WejRmnipiHLix7v4snKwt27N9A6QbvmvXuJffxxkn7zG+IeeKBduKYkVVoQBEEQgmBYXmq5jfyFiDc2lohiX6l/F3WoqKhb4wUwMo+qsWPZts2wmBR88QWuQYOIffppbQwNiBc9TdqTlYUnIwPbmjWYw1zrJXr2bOL+/veAasOVV16Je8iQsF6nqYjlRRAEQRCCUSdgVxcvNfEroVteAmNeInzhMQ5s2D//HMXjgfh43IMHaxad6GigEcuLLl4yM/H06QOAdds2Y7tlxw4S/vAHzHv2hPiCA7H++CPxjz2G4nDgOOUU3N27a9c9cKBZ5wsnIl4EQRAEIQh1A3ZruY0Ad69efjEvobuNbLYay4vtq6+0lUOHoudT626jhhoz6qLEk5WFY/x47bzffKOZgFSVhDvvJGrRImJeeinUlxyA/YsvAK1DduHbb+PyxeT4Zzi1FW3qNvrxxx954403OHLkCH379uX3v/89WVlZZGdnM3v2bPLy8jjrrLO4+uqrUZS6HwpBEARBaC38U6XdbqiurtujqPjvf8eZ5obX6nMbNS5eTHotl6FDje1e3fISLGDX6yVu5kyiPvgAANfQobiOPx5PUhLmo0eJWLsWU2EhERs2ABCxdm0TX7lvjF9/rY3x3HNBUYwMKEtXtrzk5eXx4osvcuWVV/LSSy+RnJzMyy+/jMvl4umnn6ZPnz48+eSTHDx4kBUrVrTVMAVBEIQuir940UUI1HIbJSRg6t8TCG55qUmVrhWwG6EtO7DVrPQTL2pkpDaGIJYX24oVxMydC0DZbbfhOOssMJtxnHkmAPZPPjFiZgAs27ejlPl1r3Y6SbzxRhJvuEFrvhQEU2Eh1k2btDGefjpQU3vGvzBeW9Fm4iUnJ4crrriCk08+mYSEBM4991x2797N+vXrqays5De/+Q3p6elcccUVfPnll201TEEQBKGL4p8qrce7WK2q0RFaR49fCSZecnLMAKSleQLW1wTsRtas9BcvDcS8WHzuoqrJkym7917D1VR99tkAxMyZg3X3bjyJiXjS01FUFeuGDUSsXo151y5iZ80i8uOPiVy6FPPevUFfu23lShRVxTV0KN7UVEBzT0H7EC9t5jY68cQTA5YPHTpEeno6+/fvZ+DAgdh8n45evXpxsIGJcrlcuPxsdYqiEKkr1jC7mvTziQurZcg8thyZw/Ag8xgeOus8Ghk2djuVlZoIiYlR67xOXcy4XEqdbfv3a8f17u0J2KYfUxURC3p9mKFDa/bxEy+1z2kuLATAm5YWsM15+umoJhOK14tqtVL84otEvfMOkUuWEPfUU4YbSfU7xrpzJ97+/eu8dt1l5DjzTOMa/paXtn6v20WqtNvt5sMPP+SCCy4gPz+fFF9xHtC+DCaTifLycmJ8vR78Wbx4Me+//76x3KdPH55++umAc4Sb9PT0Vjt3V0LmseXIHIYHmcfw0Onm0VfPJD4tDbtdu6fExZnI8BWE0/EZJlAUW8A2rxeys7W/x47thv9hvvARnLp4iYmBHj1I10VBT80VZa2urnM9fNaY6D59iPbflpEBF1wAS5ei/Pe/dPv1ryE/H5YsMYQLgKKqYLGA203SoUNQ+/wA69cDEHPRRcTo231ZV+bCQjLi4gyB1Ra0C/Hy9ttvY7fbmThxIm+//TZWqzVge0REBM5gpQuBqVOnMmXKFGNZV4MFBQW43e6gxzQXRVFIT08nLy8PtR0U6emoyDy2HJnD8CDzGB466zwmlZRgA4ocDvbtKwS6ERnpIjf3SMB+FRU2IImyMie5uYXG+pwcEw5HGhaLitWaR25uzTFlZVYgmSpF8xQ4+/cnQlGMObRUVZECeMvLOex/IJCYnY0dKLbbqfLb5nKB9fnnMT36qObqyc3F2r8/yb7tnuRkit58E8uOHZgPHCD2mWeo+uknimudH6+X9OxsFCA/Ph6Pvl1VSYuNxVRWRsHatbgHDGjmzAbHYrGEbHhoc/Hy888/s3z5ch5//HEsFgsxMTEcqBXJXFVVhcUSfKhWq7WO2NFprS+Rqqqd6gvaVsg8thyZw/Ag8xgeOt08+qVK+7cGqP0aLRZtWctQrtm2b5/mMsrK8mA2qwGFaSMitEDZapOvTcC4cURQM4dePfyhoqLO9UwFBQB4u3Uztn37bQTTpnXj/vtLuemmCMNq5Bw6FNVmQ3E4KLvjDpzHHYfzuOOwff65NvYdO+qe/8gRFKcTVVFwp6YGVNT1ZGVh2rYNU3Y2ahB307GiTeu8HD58mOeff54ZM2aQ5QsE6t+/Pzt37jT2yc/Px+VyBXUZCYIgCEJr4V9hVxcvelNGf/TMIZerdryL9tDdu3ddL4B/qvThr7+m7L77ArZ79fYATmedHGyTL+bF062bsW7ZMjuqqvDCCzG65tIHR8kjj1A+fTqVV11lrHYPGgSAZfduqOWlMHompaVBLeNAewnabTPx4nQ6eeqppxgzZgxjxoyhurqa6upqBg8eTGVlJV/7goWWLFnCiBEjMJmknp4gCIJw7AiWKl075Rn8quU6AsWLbnnp1ctT+xAjYNfhAE///nVEghoVxRx+y428jLO4KmCb6YjmtvImJxvrdu/WhFJRkZmPP44M2L/y6qspffTRmoGiVeX1RkWhOJ11+iEZlXt9FXX9cbcT8dJmbqMNGzaQk5NDTk4OX/iq+AG88MIL3HjjjTz//PP897//xev18tBDD7XVMAVBEIQuihLgNqpbXVfHaq1xG/mzb1/jlheHQ0FVjWznGiIiuIE5ACS/ksOtPsOMUllpVN31Fy87d9bczt98M4pLLgkUPHUwmXAPHEjEhg1Ytm/H7ecC0i0vwcRLe7G8tJl4GTt2LO+++27QbampqTz//PPs3r2bQYMGERcXd4xHJwiCIHR1gtV5CeY20oVIXbeRniZdv3hRVQWXizq1Y7xqzbkWfpzArfeVADUuI9VmQ/WFU1RVKRw8qN3OTSaVH3+0sXOnhQEDGk5aMcTLjh1alpKPUMRLW1fZbfOA3fpISkoiKSmprYchCIIgdFFCdRvpHh//pFhVrYl5CeY20ovUgSY+aouXkpIa8bJjXzQ5OWVkZnoNl5GnWzfDXLN7tyaSEhM9DBzo5vvvbWzZYm1UvLh8cS/WX34JWF+feHn00Tiyt1zMm1NXYB4R3kyjpiKBJIIgCIIQBF28YLeH5Dbyr7BbVGSitFQ7pmfPuiIiIgLMZu24qqq6Bd8KCwNvzwsX+ho1Bol32bVLU0/9+7sNoaRbfRrC3bcvAOYQYl6OHDHx0ksxLF2VzIKz/03F737X6PlbExEvgiAIglAbVQ3qNvLva6SjW038xUt2dk1bgMjIOoegKBAVpYmXysq64qWoKFB8fPqpViBOdxsFihfNwjNggNsQSvr1G8LTuzcAlv37A9KhDcuLXkkP+OabGtPQggVRjZ67tRHxIgiCIAi18cs3Vm22RrKNaiwvugbQrS5JScEbHwJERtYvXnTLiwnNklJQoC2bg1he9GDdQMtL41Ehbl+5f1NZGaaiIt9KN6b8fCDQ8rJyZY14+fZbW0iWndZExIsgCIIg1MLoa4Qe81K/20gPvgUt9RkwxI5uXQmGvi2Y2+joUe16fdGaMOpuKyPmJUiadP/+TbO8EBmJx9fSwbx/v/b/4cNGbyRdIKlqjXjp1k0TR++807bWFxEvgiAIglALI1jXbAar1RAjwcSLbnmBGtdRjaWmcctLVVXdW3Ft8VJWpll1jJgXX4E6jwf27KlxG+mWl0OHzNTTVScAd69egM91hJ/LKCMDfPXVdu+2kJtrxmZTuf/+UkATL566ccjHDBEvgiAIglAL/xovQEgxL1BTqK4hN5NOjXip323Uj93aOFSFykqljtvo8GETDoeCxaKSleUhOdlLZKQXVVU4eDCEuBefeNGDdo1gXb94F93qMnaskwsvrOJ3vyvnjTcKMbeh50jEiyAIgiDUwj9YF/Cr81JXjChKjeuoxvKi3V4bEi8NBezqlpde7MeM5goqLVXqZBsVFmoKIjnZi8mkjUW3vmRnhxD30pDlxcfWrdp5xoxxYrPBgw+WMnx4eBsfNxURL4IgCIJQC/8aL0CDMS9Q4zrS43xbannRxUsKBcShuWrKy001fY0M8VI3MFiPewklqFbPODJiXnzF5/RidABHjtRkTrUXRLwIgiAIQi2MgF2bDaezxqJSXwyLf7l/CC3mJSpK29aQ5SUp1mmIl7Jib51U6SNHtP2Sk2uERc+eLbC86OKlZ09jn5pr1P9ajjUiXgRBEAShFv6WF91lBI1bXnSRowuShrKNGkqV1sVLYopiiJeKPYUoHg+q3Y43NRWosbx06+ZvedHFSxNiXvLyoKoKc3Y2UJNGHXgNsbwIgiAIQvsloDWAdqu021Us9RgzarpEhx6w21CqtOEOyrAQj9bXqHKvZnVx9+ljZAIFFy+626hxy4s3MRFvbCygWV8svoaLHj/xIpYXQRAEQegABLO8NOQC0nsV6d6mhtoJ6NQXsFtdXRNjk9gzqsbykl0M+MSLj2DipXdvzUKyd6/Zv3BucBTFcB1FfP89isOBajIZBeoqKxUqK0W8CIIgCEK7J5h4aUiI6G6jpsS86IKntuWlqMhXTdesEtsr3hAvlYfKgUDxogfTBooXNxaLSmWliUOHQuhxNHgwAJGffAL4Kuv6uk3q4shmUxt8/ccaES+CIAiCUAsjVdpmCyntuXbAbigxL/W5jfwziLzdMwy3UVm+NiZPEMuLv1XEaoW+fTXX0Y4djbuOnCNHAhDx7bfa+YO6jDx6E+t2gYgXQRAEQaiFv+VFFxcNCZGICO3/uhV2mx6wa2QaJXnx9OhRkypd6AJqukGDv9AJDKYdMCB08eLyiRfFqwmg9h7vAiJeBEEQBKEOunjBZjPEiy42glHb8tKUmJfalpejRzVXT1KSF+cJJxAbUe07p7ZfsJiX2uJi4EBNvOhNG48cMXHJJd149tkYIw7G44EPP7RzKHmYUUkYwB2QJl3XLdUeaFySCYIgCEIXw7/CbnW1Jhr0GJVg1IgXbbnGbdRQnZfGLS9ERBA1IB22QClxeKOj8aakAFBVVRPYW1tcDBigWWl27NBiV/7zn2i++87Gd9/ZUFWFu+4q4/nnY3jmmTgGDYrlhyEjidvwHSCWF0EQBEHokARzG4VqeVHVllXYrV01N3Kk5iYqIV6zuviCT3QLTUSESmxs4HX8LS/l5Qr//W+0se3ZZ2O57754XnhBS5Hevt3K/e6HjO31xby0J0S8CIIgCEJt/BozNsXy4nQqOBzg8YRe50VPRdbRBUNKiiZe7KMHAprlxeMX76Lvl5TkrRNM27evG7NZpbTUxPPPx1BSYqJvXzf33qvFz7zxRjTV1Qq9e2si54Ut57KB44H6CtSJ5UUQBEEQ2jWG5SUyMiTLix6w63AoAWKkOZaXgoJAa0d0ehSgiZfg8S51rSI2G4YwefnlGABuuKGc224r59lni7BaVex2lTfeKOT006tRVYVvmaC5pdLTjfPoMS/tzW0kMS+CIAiCUAulqgrwxbyUNi3mRQ+stdu9mBsos1JfwG5BgXaQbnnRXUIl9lQqrrrK2E+3vNRnFRk40M3u3VbcboXhw51cdlklANOmVXHSSU48HujTx8OgQW6+/hq2nHUjRTNOMar3+l+jvYkXsbwIgiAIQi2aannxj3kJJd5FO1/wxoy13Uaxsdr/JdZkvJmZxn56YG994mXcOCcAZ59dzcKFhURG1mzr2dNDnz6axaZXL81Cs9syEMfppwecoyHrTlsilhdBEARBqEWA5cWXNR2a5aUp4qUmTsat6QdUtcZtlJKiCQbd8lJeruD11hhGGktj/u1vKzjzTAd9+7r9jSl16NVLu07tXkher8S8CIIgCEKHoemWF+1/p7Mm5qUx8eJf9E6/RkWFQlVVbcuLtp+qKgFWmvpqvOiYTNC/f8PCBWosL/v3B/ZCKi5WjMDj9iZemm152bx5MytXriQvL4/f//73fPPNN0RFRTF58uRwjk8QBEEQjjn+qdKhZBvV9DbCr5Fjw+LFZgNFUVFVxRAvutUlMtJrHK91s1ZxuxVKSxWj8F1jMS+hkpXlwWRSqa42kZ9vIi1NO19hoWbZiY/3GgHJ7YVmWV6+/fZbHn30UbKzs9m+fTsOh4OYmBjmz5/Phx9+GO4xCoIgCMIxxXAbtSjmpWFRoSh1C9Xp4iU11Ruwny5Y9Mq9AKWl2t/x8S0TLxERkJlZ13UULnHUGjRLvLz//vtcdtllPPXUU8a68847j+uuu45ly5aFbXCCIAiC0BY01fLiX+cl1JgXqJtxVF9qsh60W1ZW4zbSLTz6tpagx73s21eTHtVeC9RBM8VLfn4+w4cPr7M+MzOTo0ePtnhQgiAIgtCWNN3yov1fXV0T89JQI0ed2s0Z8/MDg3V1gltedPHS+HUaQ497yc6usbw0FlPTljRLvPTq1YvVq1cby4qvtN8333xD7969wzIwQRAEQWgrwmF5aagpo059bqPagiEuTlvWBQtAWZm2bzgtL/v3+1te2mdTRmhmwO6VV17JE088wfbt2wF49913KSwsJDs7m/vuuy+sAxQEQRCEY41/qnRoFXaDBew2ftOvXWVXd9X4x7xAXcuL11tznbi48Fle9u2rG/PSHi0vzRIvw4cP55lnnmHx4sUoisKRI0fo0aMHf/jDH+jevXu4xygIgiAIxw6XC8VXeEWNjGyy5aWmo3TT3UZ6dd3acSa6dUW3vFRUKKiqbuFpubjQWwn4W17aa4E6aEGqdPfu3bnlllvCORZBEARBaHN0lxE0r6t0SwJ2awrU1Q7YDbS86CJG61HU6GUapUcPTaAUFpqpqlKIjFTbteWl2UXqysvL2bdvHwAFBQV8+umnFBcXh2lYgiAIgtA2BIgXW6gxL9r/1dUKFRWhFamDYJaX4AG7ejp0SYm2n3+8S+2O0s0hLk41LDg5Odq522tTRmimeNmzZw933HEHixYtAqC0tJQ333yTu+66i/3794d1gIIgCIJwLNHjXbx2Oy53TZXZUCwvTich13mBQMtLYGuA4JYXvbaLnjIdjngX0GrJ6LVecnI0p0ynyzaaN28egwcPZvr06QD069eP1157jWHDhjFv3rywDlAQBEEQjiX+rQF0qwuEWmG3aW4jf8tLeTl1WgPo6NlGumjRLS/hiHfRqREvZpxOKCnRi9S1v5iXZomXvXv3MnnyZBISEox1ERERTJo0iV27doVrbIIgCIJwzFFqOjEasSgmk9pgifxgqdKhBOxGRWnio6pKITdXW+ffGkCnxm0UaHkJR40XHX/xoltdLBaV+PjwXSNcNEu8REVFBXUPZWdnExUV1eJBCYIgCEJb4V+gzj/epaHYEj3mxeVSOHpUu7UmJDQtVXrLFm1dv37uOvvpIkUXLbr7SLfIhINg4qVbN2+jjR3bgmZlG51zzjm89dZbVFVVMXjwYBRFYdu2bSxZsoSpU6eGe4wdH4cD27ff4jzpJNTIyLYejSAIQudCVVGqq8P2++pfoC6UTCOosbwAFBVpga5JSaHHvFRWKqxfr60bPtxVZ7+aInWaktBrvIRSCC9U/MVLey5QB80UL1OnTqWyspKFCxfi9uXCWywWzj//fBEvQYh59VXiHn+c0rvuovyPf2zr4QiCIHQq4u+9l8iFCyn46is8PXu2+Hz1WV4awl+86IQiXnRxsHevBYdDWzdsWF3Li+660cVLa1peDh0y+1X6bX/xLtBM8aIoCldffTWXXnopBw8eBCArKwt7M5LNy8rK+POf/8zMmTNJTU0FYOXKlSxYsIDy8nIGDhzI7373O2NbRyRizRoALHv3tvFIBEEQOh8Ra9diqq7GunlzeMRLMywvFgsoimoUjouN9TYYI6Nz0klOADZssJKdra0bNqyu5cW/SJ2qtm7MiyZe2m+aNLSgzguA3W6nf//+9O/fv1nCpbS0lKeeeoqCggJjXV5eHgsWLOBPf/oT//jHP0hOTubFF19syTDbFlXFumEDACZpWikIghB2dEuJUloa1vM1xfKiKIHWl1DdLVlZHnr3duPxKOi3wqFD64oX3fLi9WoBwXq2UTgtL2lpHkwmFadTYds2zbbRXt1GbRqG89xzzzFhwoSAdfv27WPAgAH07duX5ORkzjzzTHL1EOyOgMtF1OuvY8rJAcB88CBmn2gR8SIIghB+dEuJqaQkPCdshuUFaoJ2ARITQ7/pn3qqw/i7d293UGuK3a5iseiuI8WwvIQz5sVqhbQ0bdw//2wFOqnlpaXceOONTJ48OWBdVlYWW7ZsYe/evVRWVvLpp58yYsSINhph04l+800S/vpX4p56CgDrxo3GNlNhYVsNSxAEodOiW0pMYbK8mPyaMtZYXho/rjmWFwgUL8GCdUGz7PgH7bZGzAvUuI527dLFSweOeXn//feZOHGiUdfl/fffb3D/Sy+9NKSLp6Wl1VmXlZXFuHHjuPfeewFITU3liSeeqPccLpcLl6vmzVYUhUhfxLkSjprJfujna+i8RnzLrl0oikKEv3g5ejTsY+qIhDKPQsPIHIYHmcfw0NbzaIiXsrKwjMGIeYmKMsRLZKTa6Ln9xUtSkjfksUyY4DTiZYYNc9d7XHy8ytGjmnipcRuFd94zMz389FPNcnJy46+7LQhJvGzZsoUJEyYY4mWLnoxeD6GKl2Ds2LGDtWvX8sQTT5CVlcXixYt58skneeKJJ4JO4OLFiwPEVJ8+fXj66adJSUlp9hgaIz09PfgGVYV16wCIyM0lIyMDtm0zNpuqqsiIjwephQM0MI9CyMgchgeZx/DQJvPocmn/gGiXi+iMjJaf06wFq0Z360ZERAIAiYl27Te9Afx/2nv1iiIjI7Tf+owMOOUUWLUKzj8/loyM2KD7JSXB3r1gtSbj02v06ZNEOF6yzqBBNX9HRMApp4T3/OEiJPEyc+bMBpfDyerVq5kwYQL9+/cH4PLLL2f58uXs37+f3r1719l/6tSpTJkyxVjWBU5BQYGRxh0uFEUhPT2dvLw8VLWun9G8fz+peXn4BkDe7t2k/vRTgG/u8JYteLOywjqujkZj8yg0jsxheJB5DA9tOY9KWRm6ZKrOy6MoDDGScUeOEA2Ueb3k5ZUCcUAlubkNx9SYzcmA5m6JiCglN7ci5Gs+95yZgoJUBg3KIzc3+BxGRiYBNvbtK6KoKA4w43AUkJsbvnvd6adbWbQogfHjHdxwQwWRkR6OVdipxWIJ2fDQrFTp1sTr9VLq57esqqrC4XDg9Qb361mtVqxWa9BtrfUlUlU16LmtP/4YuLxqFaayMlS7HW9MDOYjRzAVFuLJzGyVcXU06ptHIXRkDsODzGN4aIt5VCora/4uLQ3P9fWYF5stINuosXMHxrx4mjSW9HQPI0dCbm7919HjW0pKFL/eRk27TmOccIKTr7/ON5bb69ei3YmXQYMGMXv2bD766CMSEhL44osviI+Pp2cYcvdbm4ha4sX++ecAuAYNQnE6NfEiGUeCIAhhQ493gfBlG/k3ZqwqbUq2Uc0+Tck2ChVdvBQUmHG5wttVuqPR7sTLySefzKFDh1i6dClFRUX07NmTu+++G4ul3Q21DhG+KCfVZkNxOLB/8QUA7kGDMB86BEjGkSAIQjjxFy9hq/Pilyodap0XqBuwG250oZKTo8XkKIoaUufqzkizFIGqhjf6+N133zX+VhSFyy67jMsuuyxs5w8rDkfQ1crRo1h++QWA6rPPJnLpUsy++BfX4ME1dQjE8iIIghA2AiwvrVCkril1Xvwr6rZGcTfd8nLwoCZeYmLUdtk08VjQrJc9ffp0li1bFu6xtGsivvuO5HPOgXoyqezLl6OoKq6hQ3GNHBmwzT14MJ5u3QCxvAiCIISTAPFSVgaeltclCbS8aOvak+Xl0CFNvOgtA7oizRIvgwYNIltvwtBF8CYkYN2yBZYvR6moG0Ee+cknAFRNnoy7VkCua9AgvElJgFheBEEQwom/eAEt+yhc52y65UXbx2JRWyUWRbe86G6jrhrvAs0UL9dccw3ff/89P/zwQ7jH025xDxqEu3dvcDiwrVgRsE0pL8e2ciUA1eedh8cvFdqbkIA3LU3EiyAIQitQW7yEw3XU3JgXfR+tQF2Lh1EHXay43eFvDdDRaFbMy5o1azj++OOZNWsWJ5xwAgMGDAjY3pIide0WRaF60iRiXn4Z22efUeXX1sD25ZcoDgfu3r1xDx6MKb8mzcw1eDAoSpPES9zMmXjj4yn/4x/D/zoEQRA6EbrQMJbDIV782gM0rbeRtk9rNTOs3QqgZ8/w1jLrSDTL8rJlyxYKCwsZOnQoTqeTLVu2BPzrrFSfdx7gS4HWWxJ4vUTPmwdoLiMUBW9KCqovcsvtK1cYqngxHT5MzJw5xM2ahVJerq30eIi/5x6ifNcRBEEQNOpYXpqYLm3Kz0cpKgLAsmULke+806yu0lATsNsaadJQV7z83/9V1bNn56dZlpfWrLDbnnGNHg0pKZgKCoj4/nucp5xC9CuvYFuzBq/dTuWVV2o7mkx4unfHsm+fZnkBvCEG7PpvN2dn4x46FOvGjUTPn48nKYnKa69tnRcnCILQAWmJ20iprCT1jDPA5aL4uedIuPNOTPpDI+3R8lIzhoQEL2ecETz7tSvQRZOsmonZDP/3fwDYP/sM8+7dRvfo0ocfxtOnj7Fr9dln442OxnHaaYCf5aW4uMFoeH/xYvEFRev/m4qKwhJJLwiC0FmoE7DbBPFiPnQIU0kJpspKkm64IUC4QNMtL927a7/Pffu2jjvH3/Jy7rnVAanZXY2QLS87d+7ks88+Iz8/n4SEBCZMmMC4ceNac2ztk4sugrlzsX/6KUREoDidVJ92GpVXXRWwW+kjj1D6wAPga12gixfF68VUUmIs18bfrWTevz/gf0VVGzxWEAShq9Eiy4vPXaSjKgqKXz18f8tLKOLl8ssr6dXLzZgxrpDH0BRiY2vGcMEFXddlBCFaXtauXcv999/PgQMHSEpKoqysjH/84x8sXry4tcfX/pg4EW9kJJZDh4h64w0AzZVTK7S8rExh687ImhVWK964OKBh15HJ78ukW1zMBw7UbJdsJUEQBIOWiBf999aTkUHZH//I0blzA7ZXmaI4elS7TSYnN+4KslrhtNOcIbmYmoPJBA8/XMKtt5Zx1lld12UEIVpe3nvvPc455xxmzJhhrPvyyy+ZO3cuF154IaauVOIvMhLHmWcSuXQppqoqvHFxVJ95Zp3dbrklkS++sHPrrWX8+c9lKAq4e/UiYtMmrBs24K6VoaUTYHmp5TaCQHEjCILQ1aktXrbvi2LmDYn88Y9lDBnSsPtG/z11DRpE2V131elCeCA/GlVViInxhiRejgUzZoTeqbozE5LqOHjwIOPHjw9YN378eFwuF4cPH26VgbVnHL6sI4CqCy4Auz1ge2WlwsqVNgBeeCGWhx7SLC7/zbyb6bzKW3MUioqCFwEI6jbyFy9ieREEQTDQxYtexfz1TeNZujSSV1+NbvRYU3ExAN7ERN/JFJzDh5NND0qIY+8+7fm+d293q9RtEZpPSOLF5XIRFRUVsE5fdjqd4R9VO6f67LO5UnmL8ayhcHLdmjbffx+By6UQHa0p9TlzYli5MoKbP7+a15jOrZtv44bfJgQ9t9k/YPfAAXA4jKaOIOJFEATBH73OizctDYCcUu1hcds2a6PH6pYXQ7wAq7tPZQA7OZ9P2L9fFy+SKNHeCDlgd+XKlWzatKnO+hUrVpCQkGAsK4rCr371q7AMrr2S6+jGAvUKAD53FHIegb7Hb77RrC5TplRTWqrwySeR/Pa3STjdJjJNh8jxdmfTRguqWidUJkCcKE4nEevWofhlGNUrXjwezSEqjweCIHQhDMtLejrWrVvJqdASGn75xYLHoyWJ1ocuXlTfPczthlu3/xEnNtZwMukbKwHN8iK0L0IWL5/4evfUZunSpXXWdXbxsmZNTX7aF1/aOe/82uJF237KKQ5693bzySeRVFZqRq5Zp77DFV/fTnm1FcfDLxJ5x5XGFwfqihPbN98ELAeLeVGKiki+7DJMhYWUPvggVRddJCJGEIQugSFedMtLdTIA1dUm9u41079/A6UparmN5s6NZsv+eGP7Z59pIQFieWl/hCRe3nnnndYeR4fi++/9xMsXdlS1xNAKR48qbNmimSsnTHCQlubl5JMdrF5t47jjnJw9I5WeX2ezn97kvfIlw9JNVNx0k3E+Xbx4EhMxFxXVFS+1LS8eD4m33IJ12zYAEm+9FfO+fZTfeWe4X7YgCEK7Qxcv3rQ0XFjIdacY27ZtszYsXmq5jd5+WwuHiI/3UlJiMh46xfLS/uhCaULhw9/ycviwmc2ba3yrq1fbUFWFQYNcpKVpMS+PPlrC2WdX89RTJTjPPIPe/TWls4v+WlyLjqoa4sQ1ciQAET/9BIDXFxRcW7xE/+c/2L/+Gm9kJBW+Cr9R774bxlcrCILQftFjXtw9e5JLBqrfbW3r1objXgzx4rN+Hzqk+Zhql90X8dL+EPHSRPLzYedO7QsxbpzmLvr8c5uxXY93OeWUGlfS4MFu5s07yvHHu0BRyBqnmTV30R+TX7aWUlGB4guAdo4dG3Bd14gRQF23kf2rrwAou/deSh98ENVkwpKdjSknp+UvVhAEoZ2jW17c/fuTHT04YFtjQbv+lpeKCoWyMu2WOGlSTbNHu91rPIgK7QcRL01k5Urt/yFDXFx6qfal+fLLmlTpYOKlNn36aCp+N/0w5+UZ63Writdup+L663GcdJKxzXX88QH76Fh27gTAOWYMamysIXJs333X9BcnCILQwTCaKEZFsT9LK+lht2gVbrdubTgyQvGLeTl8WLsdRkd7GT/eicmk1Xzp3dtDVypl1lGQt6SJrF6t/T9unJPTT9fU+YYNVkpKFHJyzOzda8FkUhk/vv4Ucj34q7blxRAv3bqhxsRQ+PbblN1xB9VnnUXVhRcG7ANaoK45Px/QnjoAnL56PBEiXgRB6AIY4sVuZ3+3EwA4LXUrADk5FkpK6kleqK7GpMfLJCRw+LDmMkpL8xIVpTJokPaQ2auXuIzaIyJemsjBg9r/ffu6ycz00q+fC69XYc0am5FldPzxroDun7XR/ae76K+JD69mkjTEi967yGKh7E9/4uibb+Lp2RMApaTEaM5o3bULAHdmJmpMDAAOn3ixrVkTrpcsCILQPlHVGvESGckBu/YQN9y9nrQ07XdSr9VSGyNN2mxGjYsjP1+7HerHjRqlPYD27y/ipT0i4qWJ6IaS5GTtA37qqdoHfOVKW0guI4BevbRjj9KNInesIVr0nkf+jRc//9zGzJlxlEdo0fB6c0YAy44dALgHDjT2d44bh6ooWPbuDbDqCIIgdDpcLqMOlhoZyUG1OwA9C38mPk5bX15eTzVz3WWUkACKQl6eZnlJT9eO++Mfy7j55nJuvFHK8bdHRLw0kRrxollLTjtNEypffWVj1arQxEtUlGp8QXbTD5Mv7qWO5QV44IF45syJ4bl/J+CNjw/YzxAvfn2S1Ph4XMOGAWBbtaqZr1IQBKH949/XSI2M5FCJVl23p2cvMWatwFy94qVWmrTuNkpN1X7b09O93H9/abvpaSQEIuKlifhCTEhJ0T7QJ53kwGxWyc62UFBgJjHRw+jRjbdM0P2ou+mH2aeIaouXI0dMZGdrJs///CeGnTGBQbt6sK6/5QWgeuI5qID944+b+zIFQRDaPYbLyGwGq9VIdc7iIPGeYgAjg6g2tavr6gG7uttIaN+IeGkCTifomcq6Go+LUxk5Uots79bNw3//e7R2n8ag6EG7OxhoBN3WfhLYsKEmzc/pVPhj2aOofvtZfZYXV60O1X86cCexlLHjyyMoTWgPLwiC0JHwj3dxexQjbiWLg8S5NTd8WVkjbqNalhfdKi60b5olXnb5AkVr88svv/CXv/ylRQNqzxQWatNlNqskJNSYEv/611J+/etKPvjgCCec4ArpXP36aZaXHQzElJeHcvQoEevWAVq2EcCGDVoA8IknOrFaVZaWnsa/+AOmo0dRysow5+YCgW6jdeus/GdhDyqIYbb7BuIeeYRul15KhJ4mJQiC0EnwFy/5+Sa8XgWLyUMq+cRRBkBFRcOWF1286DEvUtOlY9As8fLoo4+yZcsWY7mgoIBnn32WmTNnkpKS0sCRHZuCAm26kpO9AXn/Y8c6+cc/iunTJ3TF3revJl62Mwjrpk2knH8+1m3b8NrtOE49FaixvFx8cSUPPKBZUO7mGdZvjiLixx8BrRmZ6ouFUVWYObOmL8d7XIZ1wfvY1qwh6q23mvmqBUEQ2if+4uXoUe1HuVtUFWa8xKrab2a9lpda1XXFbdSxCLkxoz+XXHIJTz31FDfddBP79+/n448/ZuDAgTz++OP099Ub6YwcOaIp827dWq7MdcvLdgZh/+wzFLTy1kdffRVPnz6oKqxfr1leRo50cdxxLta9uZclO0/gzv+exg9LzwGg+qyzjHN++aWNdesiiIryEmVzc6SoG59yHr/iQ8yHDrV4zIIgCO0Jf/FSVaWJjyi7G8ohzquJk/oCdv0L1JWXK0YfI7G8dAyaJV5+9atfER8fzwsvvEBcXBx33303I329eDozR45oH249WLcl9OrlxqR4KVPjyCOdDPIo/etfcQ8dCsD+/WaKi01ERKgMGeJCUeDRly2smFjCRvcw5uRfxA2DvqT0oYeMc65bp4md//u/auLivLzySgRv9HmAX+390EivFgRB6CzofY3UyEgqKzWREhXpi0d0a+KlsYBdb2IieXnaPnFxWoE6of3T7IDd008/nXvuuYeqqioOd5F6IrrbKCWl5WZFmw16pmtPDdsZhDchgepzzjG26/Euw4a5iPD1gUwalMSf7tVqDtyvPMH+519HjY42jtmxQ9Oigwe7uPhi7dyfHBqFEysmPU1KEAShk+BfXbdGvGjiI96lBew2mirtV103NVVcRh2FkCwvDz/8cL3boqOjee2111i9ejVms/YBmDlzZnhG187QLS/hcBuB5jral6uJl9FT+2qKxocuRIYNCwwAvvomeOm/bg4cSGB13gAmDnfUOWbQIDcjRrhISPBSXGzmZ45j9NG14HKBteFGZYIgCB0Ff7eRIV5iNPES59TFS+OWF//WAELHICTxMtTnymjqts5GON1GAH0GmeAbTbxU/TpwHrOztS+TXo1Xx2KB8eOdHDhgYe3aCCZO1MSLwwF792pv58CBmptp5EgnX31l5zvlJEarazEVFuJNTw/L2AVBENqa4OJF+z+uWrM2N1phNzGR/M0SrNvRCEm8XHbZZa09jg5BQYEmKMIlXvr1174om4dMxXWcLWCbXpyuZ8+6fTVOPNHJe+9FGTEuAHv2WPB4FGJjvaSna+MbNconXmyncWv1C5gLCkS8CILQafB3G1VUaCIlMlYTIgkOTbwEzTZS1QDLi56plJQklpeOQrNjXsrLy9m3bx+gpUp/+umnFPuUbGdFt7yEq1y0nnG0s6pHnW31WV5AEy8A69db9R6Nhsto4EA3iu+7qhfP+847FkDiXgRB6FQolVoLADUqiqoqn3iJ034749BSpYO5jZTychS39vurJiZSVKTtk5go4qWj0CzxsmfPHu644w4WLVoEQGlpKW+++SZ33XUX+/fvD+sA2xM14iU8pkVdvBw4YMbh1w6pslIxrDzBLC+DBrmJjvZSUWFi+3ZNtGzfbvVtq4mROeEETeTsdvaikCRMBQVhGbcgCEJ7wOQnXmrcRiZUm80QL8EsL7rLSLXbUSMjKS72WWsSRLx0FJolXubNm8fgwYOZPn06AP369eO1115j2LBhzJs3L6wDbC94vTUVdsPlNkpL85KU5MHjUfj++xoX0IEDmnCJj/cSH183bc9sxqjkq7uO/C0vOomJKv36aft9zzjMIl4EQehEKAHixVfnJUrFGxtLrK/CbmWlybBQ69QuUCeWl45Hs8TL3r17mTx5Mgm+Nx4gIiKCSZMm1ds6oKNTXGzC49EUfLiyjRQFzj9fq1Pw4YeRxvr9++u3uujorqO1azXxUmN5CTxm1ChNvKzhJLG8CF0SpbyciDVrtBLUQqdCCWZ5iVJRY2IMywtgxMPo1G4NIOKl49Es8RIVFRXUPZSdnU1UVFSLB9Ue0Wu8JCWFN9v4V7/SAs6WLo3E6WtGXROsW797ShcvP/4YQUWFwr59muAZODAwtXrCBM0fNY9rUfOOhG/ggtBBiHvoIZIvvVS6rHdCdPHijY4OEC/e2FhsOImwaL+h5QcDG9Qa1XVrWV4kYLfj0Czxcs455/DWW2+xaNEitm7dyrZt21i0aBELFizg3HPPDfcY2wV6vEtaWnjPe9JJTlJSPBQXm1i1Sss40oN1GxIvo0c7URSVvXstLF1qx+tVyMx0G5lGOv/3f1Ukx1SRTS8+3DksvIMXhA6AZfduAOxfftnGIxHCTdBU6SgvamwsALEWbbv53scDjvO3vKgqfjEvYp3rKDSrPcDUqVOprKxk4cKFuH0R2xaLhfPPP5+pU6eGdYDthVGjnKxYUUBcXHgbT5rNMGVKFa+9FsN770Vx9tmOBtOkdRISVIYMcbN1q5Xnn9e+qCed5Kyzn90O1523l2feH8rz+y/ljKYO0Osl7tFHcffqReV11zX1aEFoc/QbVcR337XxSIRwEyzbSLe8AMSqJRQSQ+UvhzS3oS8V01+8VFUpOBzaenEbdRyaJV4UReHqq6/m0ksv5eDBgwBkZWVht9vDOrj2RGSkFgybkQG5ueE997Rpmnj58MNIbryxvME0aX9OOsnB1q1W9uzR3saTT3YE3e83V5Xw/PsOvneMZNOmAkaMcAXdLxjW9euJ+c9/UC0WKq+6Sir0Ch0O09GjAFj278d06BDe7t3beERCuFAqtHYpalSUEdeix7wAxLuOAplUVJpQiopQk5KAQPGiu4wiIlTpa9SBaHadF4D8/Hyys7PJzs4mv5k1RMrKyrjllluCHj9//nyeeuqplgyxQzBihIvLLtOeIP7854SQAnYBxo0LtLSMH1/X8gLQbVACF6D5+1f+cweWzZtDHlvEunUAKG43Fl9dH0HoMHi9RlosQNS775Jw221Y165tuzEJYaO+gF1vXBxQ01m6lLiA369A8VJjdVGCF+MV2iHNsry43W6ef/55vv/++4D148aN47bbbsNiCe20paWlPP300xQEyYLJzs5m2bJlPP30080ZYofjvvtK+eQTO1u2aJYNu10lM7Nhy4u/WOne3V1vjIwaF8ckPmMxF7PyUy8Pb5pO/vffE8o3NcLvR96yaxfuAQNCeTmC0C5QSktR/PJk4/7+dwBMBQUcXbCgrYYlhImgdV78LC96xlEpcVj278c1apR2nF/Arm55kRovHYtmWV7mz5/P1q1bufPOO3n11Vd57bXXuPPOO9m2bRtvvfVWyOd57rnnmDBhQp31qqryyiuvMHnyZNK7SDn71FQvf/97MePHO5g2rZKXXz5qdJOuj27dvAwYoLmAxo931q9FFIWJsZq/fw0nUZlTiiXElHarz/ICYNm5M6RjBKG9oD9h18b6yy/HeCRCa2AE7Naq82IE7PpqvZQShzmI5UWq63ZcmiVeVq1axbXXXsv48eOJiYkhKiqK8ePHc/XVV7Ny5cqQz3PjjTcyefLkOuu/+OIL9u3bR2pqKmvXrjWCgjs7v/pVNQsXFvLss8VGw8XGuPDCqoD/6yP5jgvpb8/Gg4UvOQvbypVYdu3C+uOP9R5jysvDkpNjLIt4EToaeryLNyEBb2RNLSXFGdzFKnQsGgvYDbC87N1rHBcs5kXES8eiWW4jl8tFjM8s509MTAwuV+jBoGlB8o6rq6t5++23SU9Pp7CwkJUrV7Jo0SJmzpxJRBBThMvlCrimoihE+n6klDA7MPXzhfu8LeH22yu45poqX9Xf+sdVefPNnHIgjl2vwzLOZfKHrxM7axZKRQX5q1fjzcqqc4xt/fqAZcuuXWF57e1xHjsaMoehYfa5B9w9e1IyaxZKVRXJv/oVpuJiTBUV4LvJyTy2jDb5PLrdhgh126KMjCF/y4vRIoBYLPv3G+Mz2gMkJVFSosUYJiSobfo5kO9002iWeBk5ciTz58+ne/fuhlsnLy+PBQsWMHLkyBYN6Pvvv8fhcDBz5kxiYmKYOnUqd999NytXrmTixIl19l+8eDHvv/++sdynTx+efvppUlLCm9LsT3tzZQXRHUG5+GJ4/XVNvET8eIuxPm3fPhgzpu4B27dr/59+Onz9NRG7d5ORlgamFsV5G7S3eeyIyBw2gld7mo5ITydF//1ITKSoSOXgOhOjp2nzJ/MYHo7pPJaUGH/Gpvcz/u7XL53Ifb2AQMtLRHY2GRkZ4PEYx6YMGoRziSZ0evSIIiOj7YusymcxNJolXq6//noeeeQR7rzzTpKSklAUhcLCQrp37270O2ouhYWF9O/f37DsmM1mevbsWW8209SpU5kyZYqxrKvWgoKCsLubFEUhPT2dvLw81A5YanzgQAVFSWO32p9c0skgD4DylSspO+WUOvt3+/JLIoDiKVOI//ZblIoKDq9dG9RK0xQ6+jy2B2QOQyN6717igKroaIp9NQ6SMjI5t2guP13ej49jC5k8uZvMYwtpi8+jKS+PNEA1mdiTUwykoygqRUV5VLjdJBMoXsjPJ2/nTnA6SfeNMdfh4ODBSiAKq7WU3NyKYzL2YMh3WqsXF6rhoVniJT4+nqeeeopvv/2W3bt3o6oq/fv3Z8KECVhbWAckOTkZZy1/9JEjRxg2LHh1WKvVWu81W+sDoKpqh/xwxcbWFLb7hlO41PYhisOBZfNm7fX4F3E6fNjINKo+80yi+/TBunMnlp07cWRmhmU8HXUe2xMyh4GYDx4k8t13qbzuOrxJSSiFhQB4EhONeVpk/TU/oVkaV62yMnmyzGO4OKbz6F/jxZdpFB2tAire6GigJmC3xNoNXGDauxfVF1bgjY1FtVgCYl7aw2dAPouh0Wz7v9Vq5YwzzuC3v/0tM2bM4IwzzmixcAEYNWoUOTk5LFu2jMLCQpYuXcq+ffs44YQTWnxuAcaN0wKBv+p3HcXPPguAddMmomfPJr1/f6ybNgFg//RTAJyjRuHNyDBSpCVoV2jPxPzzn8TNmkXUG28AfoGZvuJkXi88ln29sf/GjY2k9AntFv9gXf8CdUCdmJfSCO1p3rJvX70dpSVVumMRnuAFH+vWreOrr75q0TliYmK47777WLVqFbfffjtLly7l9ttvJzU1NUyj7NqMGaNZtVZFnkPVeeehms2YCwuJfeYZTNXV2Hz9XyI/+QSAKl82mHvAADyYePV/PXnoobg6LeYFoT2gi2+rL16rdvfg5cvtbC3KQkG7UW3YIBWjOyomvzTpqqqaNGmgTrZRkVkTr9atW2tqvEhH6Q5Ns9xG06ZN48knn6Rv374B691uN/PmzePMM89s0vnefffdgOWBAwfy6KOPNmdoQiOMHauJl61brZS5IkkZOBDrtm2YqqsBMOfkoBQVEbF6NQDV550HQP6Asczgc1asOxPWwVlnOTjttNDSuQXhmOByYd2xA6hpxlhbvKxZo1laruFN3uQacnPN5OW1wViFFhOsum5kpM/y4ouZzEQr9ZDj0jJb7Z9/blTf9SYnA1BcLH2NOiJhtbzExcV1mZosHZWMDC89e7rxehXWrYvAVSuWyHzwIPYVK1A8HlxDhuDp0weAFzdPZAU1onTdOnliFdoXlt27jdRZ8549WmsAvc6LT7xs3ap9bk/na4ZYNBfoTz+1wWCFFlNfawAArFa8kZH0JBuAkio7JUo81i1biJ47F4Dqs8/G6/XvKC3ipSMRsuVl//797POrULhu3ToOHDhgLDudTlatWlVvYK3Qfhgzxkl2toVvvolg8ogR4Jdqbj54EIuv+qhz7Fhj/YYd2tPKIH5hO4PZsEFiBYT2hXXrVuNvU3U15tzcgJgXVcVov3ECGxjt/o6tDOLHH+HEE9tkyEIL0MWLN5h4QbOsxB44QEKsi+IyK3uGn8/ITW9jyclBjYig6sILKS1V8HrF8tIRCdnysmXLFt59913DxbN8+XJj+d1332Xp0qWkpaVx0003tdpghfBw9tmai+jjjyOpOnsi3uhoqs86CwBLTo7ROsDdr6Z2gv7EeiP/AWD9eisSEC+0JyzbtgUu794dYHk5dMhEcbEJs1lliG0PY9CqSzdQZFpoxwS3vNQIkOK//53ixx6jew9tedewmmru1eeei5qYyJ492vN7Soqn0XYsQvsiZMvL5MmTjVL+06ZN4957760T8yJ0DCZOdGC3e9m/38LG8gGM+OUXcLnI6NcPpbqaCN+vuS5eCgtN5OWZURSVa9V5/Fl5miNHrBw8aKZHD4ncFdoHuuVFVRQUVcW6cSOKz42tJiWx9RtNgA8Y4Mbq7MaYPdrnXBpMd0wadBsBzlNPxXnqqWSt1MpD7M0cb2yrnDYNqLHEDRsWemV4oX0Q1pgXoWMQHa1y1llasO1HH9k5WmzGZbLh9bVrMPtqY+jiZcsWTeP2yawimUKO52dAs74IQntBFy/OceMAOPrtbs5nKfOs16FGRho3qqFDXbgGDGAo2v75+TVBm0LHwRAvkZFBxYtOZqb2gHXAkU7FdddROXUqjtNOA0S8dGSaJV5eeOEFevbsGe6xCMeQKVO0NMOXX45hxIgMxo9P41/WO3Gj9flQ7XY8vmJ0ustoyPEKnsRExqpah+r168XOKrQPTEeOYM7PR1UUqn0W4n/+cAafcj5/9TyOqtZ8jocNc+E67jhiKSfdrrmV9u5tVuKl0IY0ZnnRycrSxEtOjpmSxx+n+IUXwKK93/6fCaFj0STxcuTIEaqrq0lJScHie/N/+OEH3nnnHb788ksqfR8mof0zcaKDyEgvLpf2pc/LM3PngXu4jPeoxoa7Tx+jh5H/04lz7FjG8T0glheh/aDHaXl69MA1YgSlxDLXcTUAB73d2bLFEmh5Oe44AAaiZRzpsQ9Cx8EUIF4C67z40717jXjxx+OBbdu0933YMMmS7WiE9I09evQoL7zwAlu2bGHmzJkMHToUVVX55z//yXfffUdkZCQul4t33nmHmTNn0r1799Yet9BCoqNVXn/9KL/8YmXSpGq++MLGIw9Gs8QzlaksZmGffxv76k8nQ4e6cEaOZ9RnWnbSL79Y/TsKCEKbYfK5Or2pqbj79WM+11NGnLH9jTei2b9fu3kNG+bGpfrES/XPrGQcu3eb655UaNcEWF5yG3cbHTyovceFhSbWrIkgNdVLZaUJu91Lnz4iXjoaIVleXn75ZQoKCrjzzjuNIN2PP/6Y7777jv/7v//jtddeY86cOfTq1Ys333yzVQcshI9TTnEyY0YFPXp4uO66St677l3sVPEp5/NtlNaBt7oadu7Un05cOMePZyA7sOCirMzEoUMSNiW0Pbp48XTrhjcpiRettwEwOm0vAG+9FY2qKkyY4KBbNy/e5GQ8GRkMQqvEK5aXjkdT3UZ5eWbuuSeekSPT+N3vkrjiim4ADBnixizatcMR0p1n69atXH/99YwfPx673U51dTWLFy9m4MCBXH311SiKQmRkJFOmTGGHr8Kl0PE4+TQPl/M2AG/maOLlu+9seDwKyckeMjK8uIYNwxJrZ4DP3L5jh7iOhLbHsLx064bDqbDDpQWbP/FmdMB+999favztPO44BqL9Xol46XgEr/NSt1ZLSoqXiAgVr1dh/vxoPB4Fm02lulo7ZuhQiXfpiIQkXmJjYwMq53788ceUl5czzZduplNVVYVZJGyHxZOVxXW8DsDiDYOoqlL44AOtA+vkydWae8hsxjlmDMPYAsCOHfKjL7Q9/sXoCgq036CICJWhQ92MGKFV3Z06tZLjjqu5UbmOO87P8mIOb92iqioSbrsN+//+F8aTCv4oem+jyEiqqgLbA/hjMtXEvQD87nflvP/+ESIitH0lWLdjEpJ4OeOMM3jttddYunQpCxYsYOHChQwfPpzhw4cDmmjZtm0bb731lrFO6Hh4evTgFMt39GEP5VVWFi+O5JNP7ABcdFGVsZ/zpJNEvAjtCsPykpREfr72s5aS4kFRYObMUn7960pmziwNOMZ13HH0YS9m3FRWmjh8OHwuUPvy5UQtXEj8/fcjXUxbB8NtFB3doNsIasRLVJSXW24pZ9QoFy+9VMSFF1YG/LYJHYeQ7jyXXHIJbrebhQsXUllZyYgRI7j11luN7TNnzmT//v307t2ba6+9ttUGK7QuanQ0JS/PZtr/injqA7jvvnhcLoWMDI/RjRrAMWYMQ5kPwPbt4jYS2h6zn9soP1+zvKSmai6Ek05yctJJzjrHuAYPJgIXfdjLLgawZ4+F9PS6+zUHvTGkubCQiB9/xDl+fCNHCE3FP+alpEQTnnFxwcXLkCEuVq+2ccMNFXTrpn0uJk2qZtKk6mMzWCHshCRezGYzV155JVdeeSUej6eOa+jyyy8nOjqaAQMGYDJJAGdHpvq887hugsJn2U6jjsuvflWF/9vqHjjQsLzs3GGWjKNGUIqLwWxGjY1t66F0WvwtL4cPaB/W1NSGLR7e9HTUiAgGOncY4uXkk8MkXvbsMf62L12qiRdVxf7RRzjHjcObmhqW63Rl/MVLaan2AxQfH7w/0V13lXHKKQ7OPttxzMYntC5NVhrBYlpGjRrFoEGDRLh0EmJjVd57r5DJk6uIj/dy5ZUVAdvV+Hj6ppRgwUV5hZlDhzpxnJPXi33pUqNHTh1UFfvHH2PZsQO8XqJnzybmn/80NislJaSeeSYp558PetyYqmJbvtxogCm0HKOHUbduRsyLbnmp/yATnqwsI2h39+7wuUAte/caf9s/+QRUlcj33yfpppuIe/TRsF2nK2ME7EZGUVqqW16Cv+fx8SrnnuuQrKJOhAQsCEGJjFR55ZUivF4IpklN/XsysGAHWxnG9u0Wo5ZCZyNq3jwS/vpXKq68kpK//73O9ohVq0i68UZUmw3n6NHYvv0WgKr/+z88/foR+fHHmPPzAbD+/DOuE04g7uGHiZkzB096Ood//DH4BAuho6oB4kWPeWnM8gLg7tmTgXvqzziKv+cezPn5HP3Pfwi5c5+qBlheLIcOYf35Z2wrV2rLkpEZFky+gN0KJQa3W7e8SLfYroL8agoNUt991d2/v+E62r6982rgyA8+AMC6eXPQ7VFLlgCgOByGcAGI+Fnr/xS5aJGxzrZqFXGPPUbMnDkAmPPysG7a1BrD7lIo5eUoTs3dEyzmpSE8PXvWmy6tlJURPX8+9uXLiVy8OOTxmI4exVRSorUqmKiVHLAvXUrEDz8AYM7NDflcQj14PCjVWrxKsTsGAItFrTdgV+h8iHgRmoW7f3+OZyNQ0z6gs2HKzzc6bFv27aNOLq3Tif3TTwGovOQSXEOG4DzhBECzspgOHSLiu++M3SM//JDouXMB7YkfwPbFF637IroAhtXFbkeNjGyS5cXTo4chXrKzzbj8smbN2dnG3zEvv1z3/Q9GdbVhdfFkZlJ58cUARL3zDqaDOdp5Cwu16o8BA/EYbhChcfQ0aYBil1bLJy7OK7F3XQgRL0KzcPfvzyjWAfDzz51TvNg/+wzFd8MylZai+GqJ6NhWrcJUUoInNZXif/yDgs8/p8KXbWfdtInIDz5AUVVDqFi3bUNxuXCeeCLlt2kVYO1ffnkMX1HnxL9AHcDhw02zvGSSQ6SpGrdb4cCBmqAIy4EDxt/W7duxNfJeRS5eTMaAAcQ+8wwA7r59cZx1Fp9YpjCwYA3Hs5EyNCuBOS9PO8jlIvo//yFt3DjSTjgBk75eaBClVEt7V61WSqu1cg71ZRoJnRMRL0KzcPfrZ4iXPXsslJd3vkce+yefBCxb9u0LWI788EMAqi64AD0ScK39ZC7jXVLWfMqs2SkAlP/+97h9HboBKqZPp/rMMwGwbtiA6ciR1noJXQJ/8eL1wpEjTbC89OyJCZWBJq2xo7/ryLx/f8C+Uf/9b4Pnin7pJRSvF9s332jn7tuX+R+mMdn9IXvox2ZG8Dfu0c7tcx1Fv/EG8Q8/jDk3F1NFBdYtW0J5yV0eU3ExAN6EBIp9adIJCY2LVaHzIOJFaBaezExS7aVkchBVVTqf66iqyohhcfsajVpq3cz0AMzqyZMBqKxUuPTPY3ifyygjjmcKZ1BoS6fqwgtxnHYaAJ7UVKomT8abno5z+HAUVcX21VfH6lV1Sgy3UVISRUUmI3gzJaXxm5m7Rw8ABrq3ArXEi8/y4jjpJAAiNmxo8FzejIzAc/fty4IFUQCcwioAnuFuDpBliBerLzbKeC0iZEPCX7w0lmkkdE5EvAjNw2TC3bcvJ7IWgE2bOpd4MeflobjdeKOiDOHh2JnDH/6QwL/+FQNVVZgPHwbANWQIAKtW2SgpNZFlzWMoW6giiv8M/htqXByVV12FJyWF0j//GdWqZa04J0wAkKDdFuIvXvQquUlJHqwhfCTVxESIjQ0atKuL1epJk1BNJsz5+Zh873kwlPLygOXKnv0NUT/X+jtOYyXVRHIL/0Y5pLmHLLs0i483WovbMBcUND5owRAvany8n3gRt1FXQsSL0Gzc/fsb4qWzxb3o6c3e1FQ8vXsD8MTS8SxaFMXf/x5L+Tbt5uONiUFNSABg2TIbAP/XfxN38g8AXj58MR4PuEaO5MAPG7j685vo2zeD009PYeT7j5FEISMWPMYttyTg1z5MaAL+1XX1Gi9paSE+hSsK9OkTVLzolhfXoEG4+/dnK0P4z989Rh+d2pj8YqK8kZFstJyIw6GQmOgh4b8P8/CTlUSY3HzIr/j7J6O1lGqfeHH6rDsmES8h4W95KSlpuECd0DkR8SI0G3dWlhH3snlz5xIv+hO2JzUVd69efMvJvLBzirbOo7DyEy2ewtOjBygKHg8sX64FDp57VjlX8hYJphL250Xz8cd23G649dZEli6NxOlU2LXLyq7CZIpIYl9lOkuWRPHTTyHWEREC8I950S0vKSlNqDsUTLyoqhGw6+nZE+ew4VzFfGYuGM2998YHTTzSb6iFCxZQsGIFP+1NA2DkSBeuUyYw8NrjeHaqlp32xIaL+OHjUkwVFai+ZqcgbqNQUUpKAF28aO+5iJeuhYgXodl4U1MNy8vOnRajOVpnQDffe1NT8fTpw0weRsVEDGUAfLEmAahJeV63zkphoZm4OC8j7zwR18N/Zvq1xQA89FA8t92WwMcfRxIRoTJ79lHeequQxY98w2aGcbZdC/DcsKFzCcBjhb/bqCk1Xgz8xEturpnKSgVTfj5KdTWqyYQnM5Mfks5lAyMBWLgwioV/yw88h6oalhd33754srJYv157P0eNqmk5cPmUI1yPli7/3AuJ2v69e+PxxcuI2yg0gse8iNuoKyHiRWg2ntRUMsgjxXIUr1dh167OU6zO5HMbedLScPfqxS76A3AfTwDw+dZeeFE0ywvw+eea1eWss6qxRlqomDGDWx6w0revm8OHzXzwQRQWi8pLLxXxq19Vc/rpDk4628QwtnKWexkAGzeK5aU5mAKaMmo/aWlpTbO8JFFEN6v2NF9w90vEzJ4NgKd7d7BambvvHABS0Sxy9z/fl6ryGoGkVFWhOLS+Od5ETZSsW6e9nyNH1hSP8XTvzoM8ghk3Kzal8yOjcQ8YgCdFy0xbtPdENm/uPN+j1kIXiuI26rqIeBGajTdNM4sPNu8E6FTiRQ/G9aak4I2NIxftyfgy3iOWUo444lnLiYZ4+eYbLd7lzDNrGr/Z7fDMM8UAmEwqzz9fFNDFVp+/se7VgFhemotxI/OzvISSaWTQpw8AAyxaP6KDH2wl5pVXAM1lVFamsGh1bwDe5df0Zi9lxPHV4pr3Uh+DGhGBGhXF0aMK+/Zp34cTTqixvHi7d6c3+7kaLe36Ce7D3b8/3pQUVnMSVx56jksuSWb/fmnC0xCBMS/iNuqKiHgRmo3H1xl3iEfLltm5s/OIF8PykppKUZGCE02cdE+s4ByWA7CUybh79qS8XDGyrU46KbBr7bhxThYvPsKHHx7hwgsDq6qqkZF4Y2MZzU8AZGdbOHpUvpJNxd9tVFAQeo0XA5940dOldzDQ2ORNTmb5cjuVVSYGW3ZxGiu5lPcB+Pgju7Gf4mcJQFEMK1rfvm4SEmrcGd7ERFSbjXt5GoD/8Sty0o7Hm5LCx1wAQHm5iVtvTQyo9isEYmQbiduoyyK/lEKz0S0HQ9xa359OZXnRs43S0oyn+aRYB+7f/5ZJfAbA50zE06MHP/0Ugcej0LOnm8zMuk9/Y8c6OeGE4HciT1oaCZTQN0OLpdm4UawvTcLlwuSrtqqlSjcx2wjAl0022KWJ8O0Mqjn9wIGGRezswXvBajXEy/If0tCr1BvWH5/L6JdftO/CsGG13ndFwZORwRB+YRzf4cXMkkMn4U1M5DMmGbutWxfB229Hhf4auhiBAbviNuqKiHgRmo0aHY03JoYhbAM6l3jxt7wY5eYzTTiHD2cinwPwHeMpTerJmjXaU/a4cc7gJ2sAr896NSpTK1omrqOmYTyBKwre+Hgj5qVJ2UYxMXi6dTOCdncwkPKbbqL8t7+l8tpr2bBBe38H33gieT//zPCLe9CT/VQ4raxYoVlfaouXbdu093Hw4Lqi1e2z9PyadwH44Kc+FBZbWccoAK6ZrGU56TEz9aEcPYrV13urqyFF6gQRL0KL8KamMphfANi719I5apW4XDW1Q9LSjPTbtDQPruHD6cteerMXN1a+25LEd99pLqXaLqNQ8PisV6OSdgMYN0ohNIxYk/h4KqotVFTo71XTbmT+3aW3M4jKyRdQ+sgjOOO7GYXmjjvehRoXhzcr07C+LF0aXLzondYHD677hSh56inKb7yRi3pr7sLvf4rk/fcjUTFxHBs5a8A+AHbsaPhhoNv115Ny0UVY161r0mvtDOjixR2XQFmZbnkRt1FXQsSL0CI8aWn04ABRES5cLqXDBhravviCqAULgJpCYarFgjcxMSD9Vk1Kwp2Vxdlo3aCXL7cbrp7x41tgebFprrdt2zqP9epY4J91oltdoqK8xMQ07Ubm6dGD/mgF44pJJD9Ryy7bscNCdbVCbKyXvn19tX2ysriAjwEtUFtVA8WL2w07d2qfiUGD6lpePFlZlM6cif3btxk92omqKjzzTCwA57KMwZF7jGvX18ja+vPPRPykiR/r1q1Neq0dHpcLk6+acbEpEVXVxItYXroWIl6EFuFJTcWESv8k7Ya/e3cHvPmqKom33krC3Xdjys2tiXdJTgaTKcDyAuAaPtwQL/PnR+FyKWRkeOjZswmuCh960PMwj9bjJiencza5bC2CZRo1qcaLD0/PnkRSTU+0lgC7C7UO1Xrg7YgRLky+X0tPZiYnsxq7Uk1+vpkdOywB4mXfPjMOh4Ld7qVXr4Y/E9OmVQJQWamd/Hw+YQC7sVpVKitNHDoU/GEgav58429zF+tEbfLFuwCUkACA3e7FZmujAQltgogXoUXoloNBMTlAzRNnR0IpKzOCPk2FhQE1XoA6QaCOU07hLL7U9vEomM0qDzxQgtIMzaEHPacU7TYyZDpT1lZrY2QaJSYalpcmZRr50IsN1q60q1vV/NOdPZmZ2HFwqklr3LlqlS0g++WXX3Sri9sQPPVxxRWVLF1awGOPFfPC6fM4k6+wHT1M376auymY60ipqCBy8WJj2eRr8thVMOJd4uMprdDmR1xGXQ8RL0KL0G/wg62ayb0jBu3695MxlZQE9DUC6twUK6+9FmX5fCacXE1kpJc5c47WSYMOFX3+TPn5DBhQ/w1LCI6/xaNZNV58eOoRL3rPruOO8ys0l5kJwDkerdT/qlW2gHFs364H6zYeAKYocPzxLq6/vpKrT92JgtYiQP8s6LEz/tg/+wxTRYWxbO5i4kWRGi8CIl6EFqLf4Id4twAd02pg9usnYyotDcg0An/Li++J3mzGPXQoby04yvr1hzn33KYH6uro1zDn5zNwoHaD3LGj41mv2opAt1Ezquv6CCZeCgpMbN2qW15qxIsaFYUnMdHIOluzJgLPUS3V3ZuYaKRJB4t3aQhvcjKgtQgYOFATL8G+T5bdWnC325fi3dXES7ACdVLjpesh4kVoEbrlYFiVFjy4Y4cFbwd7CPK3vCilpTXVdVNTUVWMJ/raGSwWC8TGtuxHU3cbmcrKGGrTbkpieQmdQLdRC2JeMjNRTSYjc+6HHyJ48cUYXC6FkSOd9OjhqbP/8WykW2w1FRUmvs/tY4xDT5MeMqRpqXdeX4sAy5YtDKvUv091haz50CEAnKO01GpzXh5uN10mVirAbVQqwbpdFREvQovQb76Din7EalWpqDCRs89L0tVXk3jzzdSbLtGO8O/kayopMcSMJyWF0lKF6mrtB7I5sRSNocbE4I3SipGNfekPgIiXpqAEuI2aH/OC1Yp70CBOV1bSt0cVR46Y+c9/YgC44YbyOrt7MjMxoTJF0bKO3ik4G4CjlhT27tXev6FDm2Z5cQ0Zgjc2FvPRo4x76VZAs7zU/goZ4uXEE3EQwRmlH9KnTwaDBmXw7ruRTbpmR8Q/vujIEU2wJiaKeOlqiHgRWoTu9rCVHaV/Xy2ocd/c77F/9RWR//tfgDBor/h38jWVltY8zScnGy6jhAQvdnvQw1uGolB+88140tMZhuZ6O3jQQkVF13iKbinBYl6aY3kBKHzjDcr+9x6PPV1prOve3c0FF9SNZ/JkZQFwbem/AXjPcwkOIvjpQHcAevd2k5zctHF409LIX7mS6okTGcBOzIqHsjITubmBP9N6gK57wAC+iZzIKk7D69U+L19/3flTbvzdRtnZ2nvenEw/oWMj4kVoEWpcnNbPBRhi19weexbtMLbr/vn2TIDbqKQkoFeOnibdGlYXnfI//pHCBQvoxlFSFS3epiPGDrUFwWJemvteeTMzcY0axemnO/jVr7S6/7/7XQWWIG+Fe8AAAE7nazI5SBFJLGUyP25PAuDEE5te8wc0V2X12WcTgYv+UUEy+FTVsLx4unfnh+gzAFAUzTxz8GDn/9z4twbIztZeb8+enaE6ptAU2ly8lJWVccstt5DvC5KszeOPP86KFSuO7aCE0FEUyu64A4CR27Sqo9tKMo3NHUK81HYbBYiXZvTKaQZ6ZdZhqlasLliWyTHH5aK9BzDp75UzLonCQl28tHzMzz1XxOLFR/jtbyuCbq+cNo2jc+dSsHE9V8T8D4A3uYa16zW3zejRzRMvoIkSgCHm7UCgG1EpLsbka6jkycjgR8YAMGWElu138GDHLBLZFIJZXhqrpyN0PtpUvJSWlvLUU09R4Pfk68+qVavYuHHjMR6V0FQqpk/HecIJDHdqZco3MxyP72bcEcRLgNuoqKjmxzEpycg2ae0nO298PIDRJ0pP1W0rLNu2kTFoEHGPPdam42gQr9d4r/LVFFRVq7nTrVvLxUtEhNZQs97aPRERVE+ahDc5mSn/0AJnP+BCfvxRK2oXDvEyzLkBCLTC6ZlFnqQksNv5qXIYAFN7/QDA4cMmnM2/dIdAf9hwxiYaYk0sL12PNhUvzz33HBMmTAi6rby8nHnz5tHd90UW2jFmM8XPPsvgLM2cu80yguI//gkAy65dbTmykPC3vJgPHEDxRUh6ExL45hu9b1Er3xEiIvDGxDAI7Wm7rSsV2z/5BMXhIPLdd8HrxbxrF+Z9+9p0TLVRSktRfJahPIfmrklJ8TZaGC7cDJjckwvOKcWLmepqhZgYL4MGNf9m6snIAGB49Vog0PLi7zIqKDBxoDIFBS+TolZit6uoqlJvVd7OguXgQQCyIwfg8SjYbGqrW0aF9kebipcbb7yRyZMnB902b948xo4dywCfb1lo37gHDSJ69XzsdpVqt5VdscdTTjR/W3s+b74Z1X6L16mqUdclh+6M2rWIR7kfb3w8R8tsRlO+CROaX8slVLwJCUHFS+zTT5M+eDDmPXtafQw6EWu1G6e5qIiIb74h5fzzSZk4EcuWLcdsDI1hxLtER5NToLlrMjLaxn1w7wNVWCya6B050oW5BfpBTUjAGxnJULSeRTt2WI2MI3/xoncgH8wvJBXuITNTE0yd2nXk9WLO0WKB9nq19PQePRqvZCx0Ptr0jpLmS7OtzebNm9m0aROzZs3itddea/AcLpcLl6smJVFRFCIjI42/w4l+vnCft7NgscCAAW42bbKysWowi3iAvxXfBX/Wtn/44RFOPNHVruZRqajAVK1lk7zFlWxWh7OZ4fSwVuBcrVldBg1ykZamAq07XjUxkYEHtWDnffsseL0KZsVL1H//i6msDPtXX1HZr5827tacQ6+XiPXrjcX4Bx/EVKll4CTNmMGRpUtRk5LCf90mYvbLNDpwQPsp69HD06Q5Cdc89u/v5frrK3jllRjOOsvRsvMpCt7u3Rm4ewcmRaWkxERBgZm0NK/Rx8ibkWH0XRrLD5hzc8nK8rJ7t9YfS1GalqbdUo7Vd9p05AiKw4FqNrOvMh3Q4l3aw29JS2lPv4sdgXb3OOx0OnnllVe44YYbiPLVv2iIxYsX8/777xvLffr04emnnybFV/CpNUhPT2+1c3d0zj0XNm2C1z7oww5uACAr3cXBPCvz5yczZUrNvu1iHv3cWis4w/j75sInGfWGJl4mTbKS4TPltyppafTctAWb1YPDacbhyKBf5SbwBaXG5+URX2scrTKH27aBX/M77879TOFDtjCMtOzDXP+HTdzw+eVt/7Tr+5G3pKVx9GgcAEOGRJKR0fRaJ+GYx5degunTYfToOCyWuJadrE8fLLt30y+1jJ2H4ygsTOOEEwCfYIsePJitK7RO1GP4EWt+PgNPtvH111BSkkBGRkLLrt9MWv077XNdKllZFBZrAnrIEPux+X4eI9rF72IHoN2Jl4ULF9KvXz9G+apHNsbUqVOZ4ndH1FVrQUEBbnd4g7gURSE9PZ28vDzUDlB8rS248koT//pXKt99pwBJ9GYvc274mYmPXsjChSobN+aTlqa2m3m0btlCMuCMjmdVxakAHM8GNqonsHq1ts/IkUfJzW19t1FCVBSReOmXWMjW/FTWrDlK8t4lxPu2O37+maO+gM3W/CxGfvopCWhBoeajR3mGu/kY7Tu2jz58/9V45p3q4PXXi4iJabv3L3L3bhIAR0wM27ZVA3aSkkrIza1s5Mgawj2PvXpBPfkHTSI+KYkoYFDcAXYeHsaaNSUMHVpJ0u7d2IBDtgS+/lqzBp7CN6iFhSQllADxbNtWSW5uScMXCDPH6rfRvn49iYCje3e2bKkCIklOLiU3N3hWWEdC7i9gsVhCNjy0O/HyzTffUFpaynXXXQeAw+FgzZo17Nq1ixkzZtTZ32q1YrUG7wXTWh8AVVW77IerMdLTPUybVsmbb0YD8Hte5NSPvmBs8jB+ONKft96K5I47tB+atpxHpaSE5IsuMpow/pAxhbJdcSRylNWczO39ljBn97nY7SrjxzuOyTj1dOkBcblszU9l1y4zEWu+MbZbdu+uM47WmEOrL96l6rLLyJu/hsfK7wdg5k27iXlpNg/xEKtXx/Cvf0Xz5z+XhfXaTUEpLATAk5jIgW1anEePHu5mzUd7+07rGUeDbbv4iGFs325BVVUj3mNV4XCqqxUyMtwcl/szihd6JJUC8Rw4YG6z19La82g+cADQigRm76jJNGpP711LaW+fxfZKWxt+6/DII48wa9Ys/va3v/G3v/2N0aNH8+tf/5pf//rXbT00IURuvbUcu10lLqKK3/IqEevXc+uRhwF4553GXYHHAts332DdscNItf1KOQvQio5FUcU/zlnMggWFLFhQeMyavunF/gZEZQOwe5cZ23ffGdtNubn8uMrLs8/G1Km6Gk4i1mkp784xY/hr4r+oJpLTBh3khnvM3GV5jv9yNQBzXo6k8Ju2S4W37N8PgCcllQMHtBtZVlbnSJk10qVVLUB609eVxM6aZQTsLtuuxT6ddZYDNVZzH/WI0cRcTk7bBuzavvgCy44dje/YDAzx0qMH+/frcU6d4z0Xmka7s7x069YtYNlutxMXF0dcXAt9yMIxIyvLwyefFBBRkEv0s4Pw7N7NRQVLUBSV/fu1br1t7aK2/vJLwPLKohMAOJOvAK3Gy2mntb6ryB/d8jLQrAmCvRurMJWW4o2NxW2KYFLJe6y4XCtLv2uXlSVLwj8GpaoKy3Yt46ls2Cg+LjwegD89Y0OxmXD36cOFOz9gbFY2Pxzsyb+v3sHMdYltEsAb4RN2uYNPobLShKKoZGV1jmJluniZvOWfmLmHjQdSOPTsRwzCiScmls+/1zpQn322A++qRExlZfS0aw1FDx0y4/HQooyn5mJbtoyk667D3bs3+d9+G/bzm31p0gWJ/Sgu1gS8tAbomrQLy8u7775Lqq9HTm1uueUWzjjjjGM7IKHFDBzopveEFAoXLqT6vPOIpZxBidqP68aNwd18xxKLn3hxY2ZNiVbs6wxWAJp4Odbolhc9XXrPFu2J0nHaafyUeQErONMoA79ihY0wh3QBWnE6xevFk5LCN7t7UlFpIj3dwwknaNkr7gEDUICnqu8EYI7rNxx+4r3wD6QRTEePYvWJrN3pJwFaFWRbJ2nto9d6SaWA8/kEgDlDn6Lk/vv57rlPyT5gJSJC5ZRTHMZntbspD4tFxe1WjLYWx4TqauwffQS5ucQ++SQAln37MPlcXOHE4rO8/FQ9AtB6SLVl3JXQdrQL8SJ0bjy+lPhR8TsBjPoUbYl1q1ZDo/ipp/ju0kepcNmIN5cxHK08f5uIF5/lZXC55rbJVTPIPf4sSh59lHXRpwBwStZuEhK8lJSY+P778I/B6qvj4ho2jM8+0zpRTpxYbWQWuQcOBODMI4uYzMd4sPDkOyMw+dwZxwrd6uIaOJD9pdp71ZncBx6/4pxXxy0B4K3i/6Psdzcz/6fjABg/3kF0tGp8Vq3FhXTvrlkhjmWPo6h33iHxxhuhe3dDUAJUrvyZxx6LY+bMuPAIba/XsLz8kKfVeBk1qpOXExbqRcSL0Op4fVa10RFaq4e2trwolZWYffES1eefz8qRNwMwNv4XTPiq67aheEnd+QPD2QTAwuvexpuWxkav9qR5om0Tp5+u1aX55JPQzms6dIhuU6diW7as0X118eIYOpzlyzXxMmlSTVdll0+8ADzJX1Dw8q73Un55fHlogwkTunhxjh8fUOOls6DGxeHxpcyeMutM4uK8HDpk4aWXYpg7VwuGnz5dC3zXPzfmo0fJzNTFy7HzGZlrCVdPUhLrGMnY+3/N7NkxzJkTY3yWmkvkkiXEPfggitOJajazdpcWXtDcBphCx0fEi9DqeHTx4tZuOBs31lQMbQss27ejqCqelBS8ycmsXesr9pVWE3yq92Y6luhuI4BfoTX7++yLGAA2FvcFYJTjO846S4vFWbo0tPPG/vOf2H74gW7XX9/ovrp4+Sn2dPLyzERHewOqC7v79zf+HpZ6mEtP2gvAtP/NYOVXFv73P/sxuXHqgcyO8eONYN3OFvtQ+NZbHFm0CGXyWVxxhZb+/fjjcTgcCuPHO5g4UXtfdPGiFBUZMT/HUrwoFb405W7dKL/1VkoffJC/cQ+F1bHY7doXff78FgTqezwk3HUXMb6Cpe74RNZv0PyDJ554bIvxCe0HES9Cq+PV3UZlq7BYVI4cMeNzXbcJerCue/BgAH76ySdesg4a+7Sl5QXgQj4AtNiWqirYnKM9aY7KX8YZJ2vpyevXE1psg18pgQbdOx4Plm1aY8ivS7Q6S6ec4giII3H37Yvq8yE5TjmFB/9tZaR5I/neFK64OpWbb07iyiuT8LSijlBKSrD43H5OP/HSmdxGoLXccI4bB8B995Vy+eU1tUz++tdSo2mk/lk1HT3aJuLFVF6u/fGnP1F2331Un3Qy36C5OR+9XytFsGKFzXifmoo5Lw+lusb6t7Xf+ZSUmLDbvQweLOKlqyLiRWh19JiXqCMHjR+bn35qu/HoN2jXkCEUFJjIzragKCqj+mrVxVRFQY2Pb+gUrYIaH4/quyON5ifS4iooLzfx1lvRVFWbiaKCwY6f6b57DcOHa/P4ww8RjZ/XrxSu/auv6t3PvHcvpqoqvJGR/LRPc1mMGVPLLB8ZiadXLwAcp55KUpqF/138IifzLREmFzabyu7d/9/enQdGVZ59H/+eWZJMFrKQjZCQhABhEVmDCgKCiFURi0UE1OKCaNXaqrVq+9jWx1qR0gdF3Be0VqVIBQWrVeBFEVRSAREIOwkhC9kgCclMZjvvHyfnkEAC2chkkuvzFzNMhpObOzm/ue7Nypo1Zw4TmIqKCPjmGwK2bEEpK2PvXgtffdX8GbbWrCwUVcWdmIgnNs44N6szDRudzmKBhQvLefbZ47z4YhnDh5+6adcPL+1/vpFRealdsp1LL/JIxIybG3ptYuzYGlRV4b33WlZ9MR/Rtg7wxMVx/G9/Y8PkPwAwZIiLRrb4El2AhBdx3nmjo1FNJhSvlyH9KgDIzPTd9Vj18NK/vzFklJ7uJixWu5F6IyJ8s87UbDZCkwmVK8aeAOCFF7Showu652HGS9AXXzBsmBYqzjX5uaxM4XjRqZt64Lp1Z77Ibidy3jwiHnsMAFf/Afz3+8bL8hWPP07VLbdgnzoVgKBZP+FrLuWkLZr7f6FtX794cRjeOgf9hj31FPHDhhE9fTrdps3gr4M/Z9Ll0cya1Z1165oXYCx1Kmc7dlg5etRCUJCXIUM696dwRYEbbrBz3XWOes8b4cVXw0Z65aU2vGRmaj9Tw9lK4kN3c3uPNQCsWmVr0XCxvreLu18/7DNnkpmtrcSqG+BE1yPhRZx/ZjPeaG1fiiE9tTJynXP/2p2l9jwjd3q6UbkYMcKJWruXkC+GjHT6vBc1MJDpt5lRFJVjx7Qb0aALtRtT0OefGzfq0yc/m4qLCV20iJjJk+k2eBSTJ0RyyRcLsKNVQgI3boSa+vvXBH73HbZPPiGw9jyEA8njKS42Y7WqDB585oRIx5VXUj5/PgRp7+nMyMCTnIy1qoJ73c8SGuplzx4ra9dqoUQpLyfkzTcBcCcn85eQJ1nEg3hV7dfPa6+FNquN9GE/V//+rFqlnWN0xRU1XXbJrD7cWHfYKC/P3LygoKpYDhxAKW/+sQKmytpdlk8LL5eE/IC5uJjpy+cQGODlyBFLi06X15dHu3v1wuOBtWu1fnfRRe27D5PoWCS8iHahT9odEqWt8tm2DZ9M2lXsduNIAHdysvGpf/Rop3GNXh/uoKffiFz9+5NxiZe//vXUzWTg5d1Rg4Kw5OYyMlxbkrpjh9WYXxL6/PPEXXwx3RYuxLprF9+UDaCgJIi8mli+YhwApupqAmrLXk6n9n+gLz/1BgXhDQvj65SZAFxwgQtbU844NJmoeOQRAJJe/ys3X6e177/+pQ0T2D78EJPDgat/f46s28QL5l8B8PvwxZhMKhs3BrJvX9Nvavomes6+6Xz8sXaBP/2pvclf39nUHTZKSPCgKCoOh4nS0jN/vYe89hqxGRlY9uxBqa4mdMkSIu65h7iMDGLHj6foZ//DU0+FNatyc/qwkR5eBv31pzgHDyaUKi7to/UxPdA2hzFslJTE5s0BHDtmJiLCy/jxEl66Mgkvol3oy6UvCNiLyaRSVARFRe3f/fQStDcsjANl0Rw4YMViUZkwwUHN+PGUP/445X/8Y7tfl84IL4O0TfNmzapmyZLjTJ1qZ8rPPNRcqk2EvHDfRwQHw8mTJg4etIDXS9iiRSgOB85hw6i5+GK+4ArjfT/jJ0ZVx5qVxZo1QaSmJrB8uc1Y6mqfMYPCPXv4rnwg0Lw9NBxTp1Jz0UWYHA5uzl8IaDeqqpMQ8u67AFTfdBMffRxMSUUQSRzhjxW/4SdXaKto3ngjpGn/kKoae4lsdF1MYaGZbt28TJjgOMcXdl5GeCkvJ8DsIS5OG69rKICEvPkmlvx8Ql99ldDnn6fb009T89GXbCzoxxquYULWa7z4Yhh33BGJq4mjMnWHjSoqFLKytCCacbEH1xBth+afRGubEq1dG4RSXo5SeyxHU9Q9EmDlSi0QX3ONnYBzT/cSnZiEF9Eu9D0rQo/n0aePNqlw587G52sEbNlCt8cfR9FL0m3E+BTXq5ex98QllzgJD1fBaqXq7rtxDxzYpv9mc7hTtc23nKNGGc9Nm2bnpZeO062biuPyywEI/mo9+sHr27dbMRUVodTUoJrNlKxaRc24cfXCy6dchXPkSAAs+/fz6qvaUM2KFcHGYX+enj2BU6uvRo5sxh4aikLF/2gHOF6y+QV6pzhxOEysfyUfa1YWalAQVdOuN4aI7rO+glV1cceV2saFa9acfT6EqayM8N/8hqCPP8ZUXo7bZOWp9y8AtBtZZ9lZtyX0UKp4vQR8+y290Pp46XOrtMnRVVW4XPDJW1UcO6L97AWtXk3wP/5BJaFcErGLCWzgWtZwHC0I7dwZwJIlTRvOM9WpvOzZY0FVFRIS3MTFeXHV/ixNca0CtKqM+fIbiL388lOh5xz08HIyLpl//1v7mb3++q5baRMaCS+iXeiVF3NREYMGnTu8RN12G6FvvknE/fe36XWY64yf6zvIXnllx/lFWPnII5T885/Yr7++wb+vGacN/wR8/z0ZQ7Rw8cMPAcbQjyc+HiwWjgUmsZ1hAJjwsJf+7Os7GYAjO04aE5W3bg3Ak6sd2+BJSKC6WmH3bu3/pbkbgLmGDcOdnIypxsG0gdpOxWve0Ur79uuu4+tdsWRlWbHZvMzp+yUAo4O3ERCgcuKEiZycxocqQp99lpD33yfyl78E4K+Rf+a/3wcRGurl179u2k2w0woIwFs7ZBM9YwZphd8AcOzzvURPm0b3foO4f+he5v2+LxfxHXkkYKqupqbMzm1B77P3RALBwV6irSeYyfssvuEzAJ59Nuzcy5tV9VQI6dbNmJ+lz73Rw0vfw+tJT3fh8Sh8XjAUc2Ehto8+Ovf35nRiLigA4JMDF1BZaSIhwc2oUbI5XVcn4UW0C30+iamoyFjmu3Nn4/Mc9NOebZ9/Do62GxLQTyIujBloVBgmT+44Y+dqSAjOSy9tdLWTJyUFd69eKG43GcHahnJbv1VPVU8StYMbvzymDTsNDtzLaLSJuP9xTwLgw/0jjPdzOBS25cRoX9uzJz/8YMXjUYiP99CzZ53lQk2hKDiuugqAmTVvA/B58UgOKH2ovPdeXn9d+yR/4412wtK1/hCcs5+BAxuefGy8bVUVwcuXA7DdM5jxbOB3pb8F4IknyjvNYYytUXeSeS+rNgx4sPdEPNHR3MI7fHxiAgB5JDLWtIm+7COEav7lmILVqrJsWSmHb3uE95nNrWEruOSSGtxuhRUrzjHpyeFA0SddhYVRWKjdUvShK/eAAYC2V8vVl2kr0f7OzwEI/sc/zvl9mfPyUFQVr83G6x9o1duZM+2Y5M7V5UkXEO1C36jOfOyYEV527Wq88uINPVWytq1e3WbXoVde3sifgterMHSo09hS3V/UjB0LwPg1D2PGzY49oez6r/Y96EM/n2ZpO+Fe4fnMONjv1fWDqFC68Z5Dq+oEB2s3mK+LBxhf26IhozrsteFl+JalTAj/L04CuTlyNXvVdGOy5u23n8SdlgaA5dChOiunGp7EYPvgA0yVlXisgUxnBV8xHpPiZc6cKm68seNUzXypbnhJSNcCx8G0iWz79EeWcyMmPLzOHcRQxGFvCgfoC0B0lJtFi04wYoQLV+2mjQH79jJzpjYXacWK4LMP51Wd2jiP0FAKC7XQHRen9Uc1NBR37b5AswdpE8XXMokcehGwYwfWHTtYuzaQceNiuOuuM3e11lcafRt9Nd9/H4jVqnLLLVVnvE50PRJeRLvQKy/mwkIGDdJuVtnZFioqlDNf7HSe2rUTCHnrLQCKi00cPNiy/SssWVmYjxzBkpODg0Bey9R2Lr3jDv/7RahP2k3IWseN/BOAF77QDuvzJCaya5eFlV9rFZiZ7n9wJ6/RQyngwMEA+pkOsJtBBFo9/OIXWhtv9IxBNZnwxMUZw0ktPTPGNXw4nrg4TJWVLC3/Gd0o57uy/lx5ZTSqqnD55Q7S0jy4e2vHHVgOHmSIMfxVP8xasrKIuO8+whZqE4BX3vA6h0gjguNse+5j/vKXcmOX2a6u7u7MPS7V/u/377ewcaMWGEdat3MHb7LeMpmHHyjlvT9lsmv5Bn74sYhp07QAqO84bdmzh6uvdhAc7CU722IE2oboQ0be4GAwmYxho/j4Ux8IjKGj4u+YYP4KFRNvdn8IgIUPVjFnTvfajQ1t7N9/qhob8M03BH6pDS8udt4NwNSpdmJjm1kRFJ2ShBfRLvSblbmggO6UkpSkPa/Pr6jLVFJS73H19sP89tc2Ro2KY+LEWPbsad5eEaaCAmKmTCF66lTMOTn8g5spKreRkODm2mv975O789JLjZ14H+JvAKzIHU0uibgTevLkk+GoqsIM/kkG/yWGEt6LvR+zWeWYJ4Yg7Cy69hMmT9aG4zYxBmdMD1SLle+/b9l8F4PJRPWsWQD0GBzO/Lu1uS8OhwmzWeWXv9RudnrlxXzwoFF5+fFHa71jBbo99RTBK1diPn4cT1QUr+T9FIBZV+QRfX1Gy66vk1LqVEAG39KXwECV7GyLMUF63GitktJndDi//k0N4+/sScSYfvXew923L6qiYC4pIbS6mGuu0frHBx80PnSkhxe1tlKqH1cRH38qYOhDR7aVK7nD8woAr3En/8cDLMj6GQCxsdp//Jo1QaCqhP3lL0RPn07oyy+znSGsKNKGvebO9b8PG+L8kPAi2oUaEYGr9oYVsHUrw7S5pA1O2jWXlgLaduCe+Hhe4he8+0EkTqeC260YnyabKvDLL1EcDszFxZiqq3kebdLnnXdW+eX24t6oKG1eTEAAF/YsYiLr8GDhMZ7mw7zRbNwYSECAyl9M/2N8zei4fTz//HF+1vd7vmcEPw/9FwMGuAkLclJJN7ZFXsbhw2bKyswEBqrG0F5LVD74IIWZmZR8+inXPZ7K118fY/36IrZvLzSOG/DoYfb4cfp1LyI42EtVVe2ybwCn0zg5+vjf/saGV75l3VfacurZf4hBSi71meqsyuuWEsGkSVrwyMrSOvjoX6ZT9tJLnKitYjVEDQ42jn6w7N3L9ddrgefzz4MaHTrSh4308FJQUH/YCMA5dCgAATt3cj0f0jOgiIJSGw/xfwD8Jv4tHn1E23n7k09shM2fT9gLLwDgDrRxNy/jUc1cc42dCy+UXXWFRsKLaDeuEdpEUes5woupWDtjyNu9O670dP6LtsQ3Kkr7hdjYxM7GBG7caPz5CEnsYAgmk8oNN1Q3+3voKI4vXQqHDuGYMoW/8DtMeHiXm7n3ldEA3H33SVK6n9rgTg0P57rrHLx23yYGkoVl/37MZrgkKRuAr8wTjCGjwYNdrVt6bDbjTUgwAkZqqof0dDdRUafugGpwMO6EBACCsg8yeLB2U9q+3QqqSsD27ZjsdtxR3Vl0/HauubkPqqowYYKD3r39a45Seyj/859xJyRQ+rY2UXr69FN9OyTEy/CRbhxTp+KtnRPVGFd6OgARDzzAle/9gqBAL8XF5kZ3xjUqLyFasNQrL3XDS83EiVTdeisANhz8Z86rxknll5m+5OnCuVwbug6LRSUry0r+y+sBODF/Posez+Y7LiY01MsTTzR/91/ReUl4Ee3GWbsxScD33xvhpaFJu3p48cTE4O7Xj61oX6dPzty+vRm7U3m99cLLp9ROKB3uIjLSf7eTV4ODoWdP3AMHchFbeIynAahxmsjIqOGhhyrxdu9uvN5be2aSu582VGDdtQulqooxET8CsLF6BN9917r5Ls1lzLHYvZuhQ2uHjv53HbHjxhH08ce4sDA35D2e/HMENTUK48c7WLjwRLtcm79xXnQRRZmZ1EzSVpRNmFBjhP0xY2qaXGHUT7G25OURuXoFF6VrP4ubNjX8M2fMeQkNpbISqqrOHDZCUSh/6imOv/AC9quvpvtdV7FsWSmffFLMv2a+jQUPPTav4dJLtUCz3H09zsGDqZx9Cy++rk1EfvDBSnr0kLku4hQJL6Ld6OHFum0bQwdrv1j37bOcftQO5to5L97oaEqSLiQbbeO2m27SStSHD1s4caJpwwaWPXswl5biDQ7GcfnlRnjpLDuy6itE/sD/MtaymcRENy++eByLpf4KFD28uAYOxJ2SgqmigpDXX2ecRVtGvamwH59+qs1taK+20XcRtu7axQSrFjDXHx+B+dAhQpcu5VHm83buZEwmlSefLOfdd8vq3xRFo6xWmDNHq7405+iEqrlztU0OL74YgPEx2pylTZsaLsUZw0YhIdRu1ExYmJeQkDM/GNh/+lOOv/Ya3h49MJlg6FAX6uTxAASuX8+0n2rX+wL3UjZtNuvWBZKdbSE83Mstt/hvlVScHxJeRLtx9++PNzgY08mT9Dq0gYhubtxuhX37TjtcUA8vMTFsM2mBJ8V0hNRUD8nJ2gZ3O3Y0rfqiV12cF19M8WN/ZJ1Z26ht4sSOs7dLa7j79kU1mwnAxdpB9/Dtt0UkJGg3+IYqL1gsVD78MAChL73EqCMrsVHNcXswJ06YiI31MHp0+1Re6oaXSduexYqTbFI5SBrldOMV7gLg+edPcPvtVTLNpZkefLCSb745dsYp1GdlNuPMyDBWtE30rgVg8+bAeqeE6+pO2K3daqjekNG5OMeMQQ0MxJKby43mD0gmm2PE84br57zxhjaPZvbsaoKD/bdKKs4PCS+i/ZjNuGon7ylXTGJ4xVcA7Pyx/ni6Hl48MTH8UKntVzLM+1+UEycYOlS7sW7f3rQ6eOCmTYC2N8o3pQM46QkmOtrTqgmpHUpQkLGSy9OzZ70bfN3wourhBbBPnYpr4EBMlZUE5x02NrEDuPZae2P747U5I7zs2UPk1o3GdXwy5GHeZg5VhJKe7uK66/xvRVhHYDJBr14tmx/kukA7emFU/mpCQ72cOGFi9+4z573UHTbSKy/6BnVNoQYHUzNam6cV89sH+C0LAHhiYQ++/joQk0nl1ltlhZE4k4QX0a5qxo83/jyMrQDse2dHvdeY60zY/XF/aO1rt2Hdt8+YG9HU8KKfQOwaOpTvvtNK3+PG1XSqHTr1paj67ro6T0OVFwCTiRMLFuAcPpyqm25i5I0Jxl+1Z1DwpKTgDQ5GcTgw2e1cbtOC5qeRM1kSrFWHbr1VKi6+oIeXoP1ZXJyh9Ym///3MwzNNdSovp8JL8wJTzcSJ2nvZ7dwa/iHx0TW4XAomk8oDD1TKDsqiQZ3oV7jwByfnzaP0vfcgN5f0a3sB8Pnu3hQUnOqKdYeN9NVIw9mKZe9eI7yc7Vwkg91ubJvvTkvj8GGtpDBggLvNvp+OoOrmm3EOG4Z92rR6zzc050XnGjaMktWrKV+wgNGzteMBUlPdDB/ejhUpk6neIZjjRmjbx/9nQzj7q5MIC/Pys59J1cUXvPHxeKKjUbxe7r1C+3Dx7rshfPVV/bkvSgPhpUeP5oUNxxVXoFoseG02qt55jeUfnuDll8v44YdjPPhgFz+3SjRKwotoXwEBOC+7DBITGfuLFHqQT7Y7ialTo41D4PTwcjIs1liiOYxtWPfsITVVCx4FBWac55iaYcnJ0c5FCQ/HGxVFdrb2Xvq8mc7COWYMJWvW4LrwwnrP1xs2qj15uCEjR7p4551S3nmntN2rHPrQEcAFV8UYm5WFhXl58snyBid+inagKEb15arVD/GLsL8D8NB9NoqKTt02lDr7vJya89K8SdWepCRKVq6k+LPPcI0YQVqah2uvdRAVJZOzReMkvAifiegbxSbG0I+95Odb+POfu4HHg6l2k7r9VYl4vQqRIQ56UIB161aio70EBal4vYqxIVZjLIcOAbW7+yqKcWpxZwsvjWlwwm4jJk6sITW1/cvzdcOLe+xo/vnPUpYuLWXbtmPccINUXXxJDy+B33zDgsp76Ms+8kuDueWmCE6e1FKuPmzkrbPaqLnDRlB7rESfPm1z4aJLkPAifEYNCSHZdowVTEdRVNassZH1rR2ldlnDgVLt5pvW240CWHfuxFR1ksRELXzolZrGGOElNZWKCoWyMu31KSldYwy9OeHFV5wjR6IqCu7kZDy9e9Ovn5vJk2uw2aTi4muuwYONP6v3zGFN2ExiKGLn7iAeeCACVa1feTlyRHtt3XONhDhfJLwIn/LGxjKYnVw3Rqs5L3xWO2DOExnJoRxtfD21vwl3UhKK10vA1q3GBL6jR5teecnJ0YaMoqM9hIZ2jRtjvfDSrZsPr6Rx7vR0St9/n9K//122/O9gHFdcQdXPf87xJUuo/P3vSUk3s4YpWEwe/v1vG2vWBBlzXqoDwjl6VPs6X1TwRNcj4UX4lDdGmyz6yBWbMZlUPtscwyFS8cbEcOiQFjh693bjzNAO4gv47rs64eXsBzSa64SX7Gx9yKjr/GL1du+O47LLcFx2GWqdU4c7GufYsTJk0BEFBlL+9NPGRHB3cjKjyOShSzYA8Pvfh3O8XPsZzK7STo0PDfXKXBXRLiS8CJ/yxGq/9PpbDhibo33ADXijo43wkpbmNrYtD9iyxQgvTR42SkszKi9dZb4LAIpC2bvvUvbuu1LVEK3mTkkB4JGeb9Gvn4vSUjPvlE0B4PBxbWVbSopHuppoFxJehE95o6MBbW+XqZPKAC282H9yVf3KS214sW7dSlIPbXfcsw0bKSdOnDqdOjXVmKzbVea7CNHW9BOng3MP8vOfa3NdPqy+GoBDpVplLyWlC304ED4l4UX4lF55MRUXM33/Asy4+Z6RfD34DsrLte6ZkuLG3acPnshITA4HKa79wNkrL3rVxRMfjxoSwuHDXbDyIkQbcteGF3NODldd5UBRVL7xXkwuiWQXhQHy4UC0Hwkvwqf0OS/m3FxSPniRCfw/AJ57Qfsk17OnG5sNUBTctfMikj1aMCksNONuJIvUXWkE1Km8SHgRoiX0you5oID4SDsZw7Wl7B9yPYfztEM95cOBaC8SXoRP6ZWXwC1bUJxOpof8G4C1a4MA6N371Cc5Pej0qMkhIEDF42l8rxdLTg6ghZeaGozXySdDIVrGGx2tHeegqlhyc7l2tLaxywfKDHJytYNSZaWRaC8SXoRP6XNeFId28u2NI3aTlnZqi/revU99kvPExQFgLT5Gz55nn7Rrzs7WviY5mdxcC6qqEBLipXt3WQkhRIsoyqnqS3Y211s+RsHLJnUMOUe61gaQwvckvAif8tZWXnTmof1ZuLDceFw3vOiVF1Nx8TlXHBmVl+TkesukZSWEEC2nrziyHDlC2rcruI6PAPB6FQIDoUcP+XAg2oeEF+FTntrKi8514YWMGuXkN7+pICHBzaRJDuPv9KBjLioiKUnfZbfhvV7MteHFk5JinGkk812EaB298mL94QcCtmzh1zxr/F3v3nSq09pFxyZdTfhWUFC9rev1wwUfeOAkmZlF9eaoGCuTioro00cLInv3nhlelJMnMdce7uhOTpbJukK0EXfv3gDYVq5E8Xi4JL2IQYO0YV7ZZ1C0Jwkvwuf06osnKgpPQkKjr6tbeRk4UPuFuXu39YzX6VUXb0QEardudU6TlsmEQrSGfepUXIMGoXi0n6WayVfw299WEBioct11Pr440aVIeBE+p4cS14UXnnUnWKPyUlLCoP7aRnXZ2RbjhFudpfaEOH18vqudJi3E+aKGhVHy0UdU3XQT7sREqm+8kUmTajh4sJA77vD11Ymu5OyHwwjRDvRQUvcU24Z4u3dHVRQUr5dotZj4+HgKC81kZVnJyHAarzPXmazr8ZyaFyPLpIVoPdVmo3zBgnrPyVwX0d583uUqKyu59957KSoqMp7LzMzkvvvuY+bMmfzud7/jqH5cqeiUqm6/Hfs111B9881nf6HFYpyUbKozdLRrV/0MbqmzTLqgwIzTqWC1qiQkSHgRQojOwKfhpaKigvnz51NcXGw8V1hYyIsvvsjs2bN5+eWXiY6O5pVXXvHhVYrzzTVyJMdffRVPYuI5X2vMeykubnTei1F5SUkxlkknJXkwn/0cRyGEEH7Cp+HlueeeY8yYMfWey8vLY9asWYwePZqIiAgmT57MwYMHfXSFoqOpu+KosfCi7/Hi6dXLOE1aVhoJIUTn4dM5L/PmzSMuLo633nrLeG7EiBH1XpOfn098fHw7X5noqOquOBp0lRZI9uyx4PGgVVbcbsy1w4zu5GRy/p9M1hVCiM7Gp+Elrna798a43W5Wr17NNddc0+hrXC4XLtep7eQVRcFmsxl/bkv6+7X1+3Y1rWnHuuGld28PQUEqdruJnBwLaWkeTCUlKB4PqtmMGh9fp/Li6VT/b9IX24a0Y9uQdmw9acPm6dCrjZYtW0ZQUBCTJk1q9DUrV65kxYoVxuPU1FSeeeYZYmq3kj8fpBLUNlrUjrU7YYVUVhKS2IPBgyEzEwoKYrn0UiAvDwAlLo4eiYkcO6Z92ZAh4fToEd7Im/ov6YttQ9qxbUg7tp60YdN02PCyY8cOvvjiC5566ikslsYvc9q0aUyZMsV4rKfW4uJi3O62HSpQFKV2eW4hqqq26Xt3Ja1px6CgICIBZ04OpQUF9O0bTmZmMJs2VXLppScJ3LmTKMAZE0NpQQF5ebGAGYulhIIC1zne3X9IX2wb0o5tQ9qx9aQNwWKxNLnw0CHDy7Fjx1i8eDFz584l8RwrUKxWK1brmbusAuetA6iq2mU7V1tqSTt6ajt2wJYtxGRkMPjqfwMXsmuXFVVVMdWWWjxxcXi9KiUl2pz0mBhPp/w/k77YNqQd24a0Y+tJGzaNz/d5OZ3T6WT+/PlkZGSQkZGBw+HA4XDIf6YAtF14nRdcAIAlL49h+Z8CsHu3lsPNteHFGxtLRYWC06lV4qKjZY8XIYToLDpc5WX79u3k5eWRl5fHunXrjOeXLFlCbO1kTdF1qcHBlPznP4S88Qbhf/gDQ2oyAcjPt3D8uEJ47WaHnvh4iou1bB4W5iUoyGeXLIQQoo11iPCyfPly48+jRo2q91iIhriTkgCIKj5Ar15ujhyxkJVlJa2wENAqLyUl2jLp6Givz65TCCFE2+tww0ZCNIW3Rw8AzAUFdY4JsNab81JUpHXv2FgZMhJCiM5EwovwS56EBABMxcUMTNdOmN6924q5dtjIGxcnlRchhOikJLwIv+SNikINDERRVS6I16otWbstmGrPyfLExRlzXmJiJLwIIURnIuFF+CdFwVM7dDQwVDvL6MABM6oKqsmEt3t3Y5m0rDQSQojORcKL8Ft6eOntPUBAgIrdYeYIvbQjBMxmioq0YSOpvAghROci4UX4LX3eS+CxPNLStN2UdzMQT+2ZWXrlRSbsCiFE5yLhRfgtPbyY8/Pp0+dUeNEPb9TnvMiEXSGE6FwkvAi/pQ8bmfLz6ddPWy6dxQA8cXGoKsZqIxk2EkKIzkXCi/BbRuWloIC+fesPG1VUKNTUyNEAQgjRGUl4EX6r7rBRv351wkv36HpHA9hsPrtEIYQQ54GEF+G39GEjc2kpqT1OYsZNBeHkKT1lgzohhOjEJLwIv6VGRuKtPXHRVlZIWsARAPZWJtbZoE6GjIQQorOR8CL8l6LgjYkBwFRaykBlDwA7jiWwb58VgMRECS9CCNHZSHgRfs0bFQWAqayMsZ4NAHy1M47NmwMAuOgip68uTQghxHki4UX4NW9kJKCtOJrs/jcA327vxtatWngZM6bGZ9cmhBDi/JDwIvyaXnmxHD7MIHbRg3wcNSacToWEBDcpKTJsJIQQnY2EF+HX9MqL5fBhFODygK+Mvxszxomi+OjChBBCnDcSXoRf0ysv5sOHAZgU/p3xd6NHy5CREEJ0RhJehF8zKi85OQBMiNuJoqiAVnkRQgjR+Vh8fQFCtIZeeVFc2tlGsTEeFi8+gdsNPXvKfBchhOiMJLwIv6aHF+NxeDjXX2/30dUIIYRoDzJsJPyaPmykUyMifHMhQggh2o2EF+HXzqi8SHgRQohOT8KL8GunV1684eE+uhIhhBDtRcKL8G+BgXhDQ42HUnkRQojOT8KL8Ht1qy8SXoQQovOT8CL8Xt15LzJhVwghOj8JL8Lv1Q0vUnkRQojOT8KL8Hv1ho1kwq4QQnR6El6E35PwIoQQXYuEF+H39GEjb1AQBAX5+GqEEEKcbxJehN/Tw4tM1hVCiK5Bwovwe/qwkUzWFUKIrkHCi/B7zosvxpWWhn3aNF9fihBCiHYgp0oLv+eNiaH4q698fRlCCCHaiVRehBBCCOFXJLwIIYQQwq9IeBFCCCGEX5HwIoQQQgi/IuFFCCGEEH5FwosQQggh/IrPw0tlZSX33nsvRUVFxnNHjhzhscce47bbbuOdd95BVVUfXqEQQgghOhKfhpeKigrmz59PcXGx8ZzL5eKZZ54hNTWVp59+mqNHj7JhwwbfXaQQQgghOhSfhpfnnnuOMWPG1Htu27ZtVFdXM2fOHOLj45k1axbr16/30RUKIYQQoqPx6Q678+bNIy4ujrfeest4Licnh379+hEYGAhAcnIyR48ebfQ9XC4XLpfLeKwoCjabzfhzW9Lfr63ft6uRdmw9acO2Ie3YNqQdW0/asHl8Gl7i4uLOeM5utxMTE2M8VhQFk8nEyZMnCQ0NPeP1K1euZMWKFcbj1NRUnnnmmXrv0dbi4+PP23t3JdKOrSdt2DakHduGtGPrSRs2TYc728hkMmG1Wus9FxAQgNPpbPD106ZNY8qUKcZjPbUWFxfjdrvb9NoURSE+Pp7CwkKZRNwK0o6tJ23YNqQd24a0Y+tJG4LFYmly4aHDhZfQ0FByc3PrPWe327FYGr5Uq9V6RtjRna8OoKpql+1cbUnasfWkDduGtGPbkHZsPWnDpvH5UunT9enTh/379xuPi4qKcLlcDQ4ZCSGEEKLr6XCVlwEDBlBdXc2XX37J+PHjWbVqFYMHD8Zkal7OaqxS0xbO53t3JdKOrSdt2DakHduGtGPrdeU2bM73rqgdoD41Y8YMlixZQmxsLABbtmxh8eLF2Gw2vF4vf/rTn0hKSvLxVQohhBCiI+gQw0bLly83ggvAqFGjWLx4MfPmzWPRokUdJrjY7XYeeeQR7Ha7ry/Fr0k7tp60YduQdmwb0o6tJ23YPB22PhUVFUVUVJSvL6MeVVU5fPiwTKZqJWnH1pM2bBvSjm1D2rH1pA2bp0NUXoQQQgghmkrCixBCCCH8ioSXZrBarUyfPr3RfWVE00g7tp60YduQdmwb0o6tJ23YPB1itZEQQgghRFNJ5UUIIYQQfkXCixBCCCH8ioQXIYQQQviVDrvPS0dz5MgRXnrpJQoLC5k4cSI333yzcYK1aNybb77JZ599ZjyOi4vj+eefl/ZsgsrKSh599FH++Mc/Gps4nq3dpE0b1lA7NtYvQdrxdJmZmbz99tuUlJTQu3dv7rnnHhITE6UvNlNj7Sh9sWWk8tIELpeLZ555htTUVJ5++mmOHj3Khg0bfH1ZfuHQoUM8+uijLF26lKVLl7JgwQJpzyaoqKhg/vz5FBcXG8+drd2kTRvWUDtCw/0SpB1PV1hYyIsvvsjs2bN5+eWXiY6O5pVXXpG+2EyNtSNIX2wpCS9NsG3bNqqrq5kzZw7x8fHMmjWL9evX+/qyOjyPx0Nubi4DBw4kJCSEkJAQbDabtGcTPPfcc4wZM6bec2drN2nThjXUjo31S5B2PF1eXh6zZs1i9OjRREREMHnyZA4ePCh9sZkaa0fpiy0nw0ZNkJOTQ79+/QgMDAQgOTmZo0eP+viqOr6cnBxUVeXhhx+mrKyMgQMHctddd0l7NsG8efOIi4vjrbfeMp47W7tJmzassXZsqF9GR0dLO55mxIgR9R7n5+cTHx8vfbGZztaO0hdbRiovTWC324mJiTEeK4qCyWTi5MmTPryqji8vL4+kpCR+9atfsWjRIsxmM6+++qq0ZxPExcWd8dzZ2k3atGENtWNj/RLkZ/1s3G43q1evZvLkydIXW6FuO0pfbDmpvDSByWQ6Y9fDgIAAnE6nj67IP4wdO5axY8caj++44w7uu+8+EhISpD1b4Gz9UPpo0zXWL6urq6Udz2LZsmUEBQUxadIkli1bJn2xheq2o8Vikb7YQlJ5aYLQ0FAqKirqPWe327FYJPs1R0hICKqqEhERIe3ZAmfrh9JHW07vlydOnJB2bMSOHTv44osv+NWvfnXO/iZt2LjT2/F00hebTsJLE/Tp04f9+/cbj4uKinC5XISGhvrwqjq+t99+m82bNxuPDxw4gKIo9OrVS9qzBc7WD6WPNl1j/bJ79+7Sjg04duwYixcvZu7cuSQmJgLSF1uioXaUvthyEl6aYMCAAVRXV/Pll18CsGrVKgYPHozJJM13NikpKSxbtozdu3ezc+dOli5dymWXXcaQIUOkPVvgbP1Q+mjTNdYvAwMDpR1P43Q6mT9/PhkZGWRkZOBwOHA4HPTv31/6YjM01o7JycnSF1tIDmZsoi1btrB48WJsNhter5c//elPJCUl+fqyOrz33nuPL774gqCgIEaNGsWsWbMICgqS9myiGTNmsGTJEmNztbO1m7Rp405vx8b6JUg71rVlyxYWLlx4xvNLliwhOztb+mITna0d165dK32xBSS8NENZWRkHDx4kPT2dbt26+fpy/J60Z8ucrd2kTduGtGPTSF88/6QdGybhRQghhBB+RQbPhBBCCOFXJLwIIYQQwq9IeBFCCCGEX5HwIoQQQgi/IuFFCCGEEH5F9hkWQrSrXbt28cQTTzT4d9OnT2fGjBn1Xrd8+fL2vDwhhB+QpdJCiHZlt9vJz88H4IMPPqCgoID7778fgMjISKKiouq9Li0tzWfXKoTomKTyIoRoVzabzQgkYWFhlJaWNhhQ6r5OCCHqkjkvQgghhPArEl6EEB3Srl27jPkvuqKiImbMmMGaNWuYO3cuDz30ED/++CP33HMPd911FwcOHACgpqaGN998k7lz53LbbbexaNEiKioqfPFtCCHOAwkvQgi/k5mZyX333Ud+fj5Llixh7ty5hIaGsmHDBgBee+01MjMzmTt3Lvfffz+5ubkNHownhPBPMudFCOF3Zs+eTXp6OpGRkUyaNInhw4ezefNmampqKCoqYuPGjTz00EOMGjUKAI/Hw4IFCygqKjJOlhZC+C8JL0IIvxMZGQmAoijG6iRFUQA4cuQIqqo2WGkpKCiQ8CJEJyDhRQjRKf3ud78jIiKi3nMSXIToHGTOixCiU0lKSgLA7XaTkpJCSkoK4eHhfPzxx5SUlPj46oQQbUEqL0KITiUuLo5x48bxxhtvYLfbiYyMZNWqVeTm5nLnnXf6+vKEEG1AwosQotO58847effdd3n77bdxOp3079+fxx9/HJvN5utLE0K0ATkeQAghhBB+Rea8CCGEEMKvSHgRQgghhF+R8CKEEEIIvyLhRQghhBB+RcKLEEIIIfyKhBchhBBC+BUJL0IIIYTwKxJehBBCCOFXJLwIIYQQwq9IeBFCCCGEX5HwIoQQQgi/8v8Bc+utBw0Mx7IAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画出真实数据和预测数据的对比曲线\n",
    "plt.plot(real_stock_price, color='red', label='Stock Price')\n",
    "plt.plot(predicted_stock_price, color='blue', label='Predicted Stock Price')\n",
    "plt.title(stockname)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Stock Price')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 模型校核"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "均方误差: 0.69369\n",
      "均方根误差: 0.83288\n",
      "平均绝对误差: 0.57088\n",
      "R2: 0.86765\n"
     ]
    }
   ],
   "source": [
    "MSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)\n",
    "RMSE = metrics.mean_squared_error(predicted_stock_price, real_stock_price)**0.5\n",
    "MAE = metrics.mean_absolute_error(predicted_stock_price, real_stock_price)\n",
    "R2 = metrics.r2_score(predicted_stock_price, real_stock_price)\n",
    "\n",
    "print('均方误差: %.5f' % MSE)\n",
    "print('均方根误差: %.5f' % RMSE)\n",
    "print('平均绝对误差: %.5f' % MAE)\n",
    "print('R2: %.5f' % R2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
